In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_log_error
In [2]:
df=pd.read_csv(r"C:\Users\prabha\Desktop\Dataset\energy-pop-exposure-nuclear-plants-locations\energy-pop-exposure-nuclear-plants-locations_plants.csv")
df
Out[2]:
FID Region Country Plant NumReactor Latitude Longitude p90_1200 p00_1200 p10_1200 ... p10r_600 p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300
0 0 Europe - Western SWEDEN AGESTA 1 59.206022 18.082872 187382000 188684000 188250000 ... 8972550.0 5013240.0 5227700.0 5471110.0 3426880.0 3596030.0 3764920.0 1586350.0 1631670.0 1706190
1 1 Europe - Western SPAIN ALMARAZ 2 39.808100 -5.696940 136675000 147718000 163429000 ... 19453700.0 17756500.0 18187800.0 20185200.0 10986000.0 11415400.0 12689800.0 6770480.0 6772380.0 7495340
2 2 America - Latin BRAZIL ANGRA 3 -23.007857 -44.458098 99195200 113894000 127898000 ... 18605600.0 39546400.0 44701700.0 50210600.0 32788500.0 37064600.0 41648300.0 6757940.0 7637110.0 8562300
3 3 America - Northern UNITED STATES OF AMERICA ARKANSAS ONE 2 35.310320 -93.231289 117830000 132729000 146482000 ... 9498240.0 5603180.0 6226360.0 6866840.0 3779400.0 4198920.0 4633770.0 1823770.0 2027450.0 2233070
4 4 Europe - Western SPAIN ASCO 2 41.200000 0.566670 271854000 287134000 308922000 ... 17594700.0 14398500.0 15095600.0 16830700.0 11215400.0 11773600.0 13151300.0 3183180.0 3322050.0 3679470
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 271 Asia - Far East CHINA YANGJIANG 3 21.708333 112.261111 557032000 629419000 679747000 ... 86375000.0 68115100.0 87136800.0 93428600.0 41415700.0 53433600.0 57372000.0 26699400.0 33703200.0 36056500
272 272 America - Northern UNITED STATES OF AMERICA YANKEE 1 42.727839 -72.929108 123430000 131527000 145162000 ... 9951640.0 35873400.0 37247000.0 41124300.0 32253500.0 33508100.0 37010000.0 3619890.0 3738910.0 4114260
273 273 Asia - Far East KOREA, REPUBLIC OF YONGGWANG 6 35.411130 126.416190 649425000 710033000 750775000 ... 35715500.0 42866100.0 46496100.0 48569700.0 36191700.0 39809100.0 41589500.0 6674350.0 6686990.0 6980200
274 274 Europe - Central and Eastern UKRAINE ZAPOROZHE 6 47.511809 34.585460 273432000 274834000 272561000 ... 22931000.0 20624400.0 19361900.0 17990600.0 13838800.0 12978800.0 12067100.0 6785590.0 6383160.0 5923510
275 275 America - Northern UNITED STATES OF AMERICA ZION 2 42.446428 -87.803003 162525000 176001000 194245000 ... 11318900.0 19702800.0 21460200.0 23696900.0 16653600.0 18285600.0 20199900.0 3049180.0 3174560.0 3497080

276 rows × 61 columns

In [3]:
missing_values = df.isnull()
missing_count = missing_values.sum()
missing_percentage = (missing_count / len(df)) * 100
print("Missing and Null Values:\n", missing_count)
print("Missing and Null Values (%):\n", missing_percentage)
Missing and Null Values:
 FID           0
Region        3
Country       0
Plant         0
NumReactor    0
             ..
p00u_300      0
p10u_300      0
p90r_300      0
p00r_300      0
p10r_300      0
Length: 61, dtype: int64
Missing and Null Values (%):
 FID           0.000000
Region        1.086957
Country       0.000000
Plant         0.000000
NumReactor    0.000000
                ...   
p00u_300      0.000000
p10u_300      0.000000
p90r_300      0.000000
p00r_300      0.000000
p10r_300      0.000000
Length: 61, dtype: float64
In [4]:
df.dropna(axis='columns', inplace=True)
In [5]:
columns_to_drop = ['FID', 'Plant', 'NumReactor']
df.drop(columns=columns_to_drop, inplace=True)
df
Out[5]:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p10r_600 p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300
0 SWEDEN 59.206022 18.082872 187382000 188684000 188250000 121178000.0 122299000.0 122399000.0 66203700 ... 8972550.0 5013240.0 5227700.0 5471110.0 3426880.0 3596030.0 3764920.0 1586350.0 1631670.0 1706190
1 SPAIN 39.808100 -5.696940 136675000 147718000 163429000 87539700.0 93943200.0 103704000.0 49135800 ... 19453700.0 17756500.0 18187800.0 20185200.0 10986000.0 11415400.0 12689800.0 6770480.0 6772380.0 7495340
2 BRAZIL -23.007857 -44.458098 99195200 113894000 127898000 67751500.0 77821400.0 87432200.0 31443800 ... 18605600.0 39546400.0 44701700.0 50210600.0 32788500.0 37064600.0 41648300.0 6757940.0 7637110.0 8562300
3 UNITED STATES OF AMERICA 35.310320 -93.231289 117830000 132729000 146482000 92609500.0 104781000.0 115697000.0 25220200 ... 9498240.0 5603180.0 6226360.0 6866840.0 3779400.0 4198920.0 4633770.0 1823770.0 2027450.0 2233070
4 SPAIN 41.200000 0.566670 271854000 287134000 308922000 190825000.0 200465000.0 215092000.0 81029500 ... 17594700.0 14398500.0 15095600.0 16830700.0 11215400.0 11773600.0 13151300.0 3183180.0 3322050.0 3679470
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 CHINA 21.708333 112.261111 557032000 629419000 679747000 250848000.0 287891000.0 309439000.0 306184000 ... 86375000.0 68115100.0 87136800.0 93428600.0 41415700.0 53433600.0 57372000.0 26699400.0 33703200.0 36056500
272 UNITED STATES OF AMERICA 42.727839 -72.929108 123430000 131527000 145162000 103377000.0 110101000.0 121564000.0 20052600 ... 9951640.0 35873400.0 37247000.0 41124300.0 32253500.0 33508100.0 37010000.0 3619890.0 3738910.0 4114260
273 KOREA, REPUBLIC OF 35.411130 126.416190 649425000 710033000 750775000 404604000.0 440983000.0 464987000.0 244821000 ... 35715500.0 42866100.0 46496100.0 48569700.0 36191700.0 39809100.0 41589500.0 6674350.0 6686990.0 6980200
274 UKRAINE 47.511809 34.585460 273432000 274834000 272561000 162035000.0 163493000.0 162347000.0 111397000 ... 22931000.0 20624400.0 19361900.0 17990600.0 13838800.0 12978800.0 12067100.0 6785590.0 6383160.0 5923510
275 UNITED STATES OF AMERICA 42.446428 -87.803003 162525000 176001000 194245000 131460000.0 142393000.0 157233000.0 31065000 ... 11318900.0 19702800.0 21460200.0 23696900.0 16653600.0 18285600.0 20199900.0 3049180.0 3174560.0 3497080

276 rows × 57 columns

In [6]:
from sklearn.preprocessing import LabelEncoder
In [7]:
# Label Encoding
label_encoder = LabelEncoder()  # Create a LabelEncoder instance
categorical_cols = [ 'Country']  # Specify the categorical columns to be encoded

for col in categorical_cols:
    df[col + '_encoded'] = label_encoder.fit_transform(df[col])  # Apply label encoding to each column

print("Label Encoded Data:")
df
Label Encoded Data:
Out[7]:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
0 SWEDEN 59.206022 18.082872 187382000 188684000 188250000 121178000.0 122299000.0 122399000.0 66203700 ... 5013240.0 5227700.0 5471110.0 3426880.0 3596030.0 3764920.0 1586350.0 1631670.0 1706190 28
1 SPAIN 39.808100 -5.696940 136675000 147718000 163429000 87539700.0 93943200.0 103704000.0 49135800 ... 17756500.0 18187800.0 20185200.0 10986000.0 11415400.0 12689800.0 6770480.0 6772380.0 7495340 27
2 BRAZIL -23.007857 -44.458098 99195200 113894000 127898000 67751500.0 77821400.0 87432200.0 31443800 ... 39546400.0 44701700.0 50210600.0 32788500.0 37064600.0 41648300.0 6757940.0 7637110.0 8562300 3
3 UNITED STATES OF AMERICA 35.310320 -93.231289 117830000 132729000 146482000 92609500.0 104781000.0 115697000.0 25220200 ... 5603180.0 6226360.0 6866840.0 3779400.0 4198920.0 4633770.0 1823770.0 2027450.0 2233070 33
4 SPAIN 41.200000 0.566670 271854000 287134000 308922000 190825000.0 200465000.0 215092000.0 81029500 ... 14398500.0 15095600.0 16830700.0 11215400.0 11773600.0 13151300.0 3183180.0 3322050.0 3679470 27
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 CHINA 21.708333 112.261111 557032000 629419000 679747000 250848000.0 287891000.0 309439000.0 306184000 ... 68115100.0 87136800.0 93428600.0 41415700.0 53433600.0 57372000.0 26699400.0 33703200.0 36056500 6
272 UNITED STATES OF AMERICA 42.727839 -72.929108 123430000 131527000 145162000 103377000.0 110101000.0 121564000.0 20052600 ... 35873400.0 37247000.0 41124300.0 32253500.0 33508100.0 37010000.0 3619890.0 3738910.0 4114260 33
273 KOREA, REPUBLIC OF 35.411130 126.416190 649425000 710033000 750775000 404604000.0 440983000.0 464987000.0 244821000 ... 42866100.0 46496100.0 48569700.0 36191700.0 39809100.0 41589500.0 6674350.0 6686990.0 6980200 17
274 UKRAINE 47.511809 34.585460 273432000 274834000 272561000 162035000.0 163493000.0 162347000.0 111397000 ... 20624400.0 19361900.0 17990600.0 13838800.0 12978800.0 12067100.0 6785590.0 6383160.0 5923510 31
275 UNITED STATES OF AMERICA 42.446428 -87.803003 162525000 176001000 194245000 131460000.0 142393000.0 157233000.0 31065000 ... 19702800.0 21460200.0 23696900.0 16653600.0 18285600.0 20199900.0 3049180.0 3174560.0 3497080 33

276 rows × 58 columns

In [8]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 276 entries, 0 to 275
Data columns (total 58 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Country          276 non-null    object 
 1   Latitude         276 non-null    float64
 2   Longitude        276 non-null    float64
 3   p90_1200         276 non-null    int64  
 4   p00_1200         276 non-null    int64  
 5   p10_1200         276 non-null    int64  
 6   p90u_1200        276 non-null    float64
 7   p00u_1200        276 non-null    float64
 8   p10u_1200        276 non-null    float64
 9   p90r_1200        276 non-null    int64  
 10  p00r_1200        276 non-null    int64  
 11  p10r_1200        276 non-null    int64  
 12  p90_30           276 non-null    float64
 13  p00_30           276 non-null    float64
 14  p10_30           276 non-null    float64
 15  p90u_30          276 non-null    float64
 16  p00u_30          276 non-null    float64
 17  p10u_30          276 non-null    float64
 18  p90r_30          276 non-null    float64
 19  p00r_30          276 non-null    float64
 20  p10r_30          276 non-null    float64
 21  p90_75           276 non-null    float64
 22  p00_75           276 non-null    float64
 23  p10_75           276 non-null    float64
 24  p90u_75          276 non-null    float64
 25  p00u_75          276 non-null    float64
 26  p10u_75          276 non-null    float64
 27  p90r_75          276 non-null    float64
 28  p00r_75          276 non-null    float64
 29  p10r_75          276 non-null    float64
 30  p90_150          276 non-null    float64
 31  p00_150          276 non-null    float64
 32  p10_150          276 non-null    float64
 33  p90u_150         276 non-null    float64
 34  p00u_150         276 non-null    float64
 35  p10u_150         276 non-null    float64
 36  p90r_150         276 non-null    float64
 37  p00r_150         276 non-null    float64
 38  p10r_150         276 non-null    float64
 39  p90_600          276 non-null    float64
 40  p00_600          276 non-null    float64
 41  p10_600          276 non-null    float64
 42  p90u_600         276 non-null    float64
 43  p00u_600         276 non-null    float64
 44  p10u_600         276 non-null    float64
 45  p90r_600         276 non-null    float64
 46  p00r_600         276 non-null    float64
 47  p10r_600         276 non-null    float64
 48  p90_300          276 non-null    float64
 49  p00_300          276 non-null    float64
 50  p10_300          276 non-null    float64
 51  p90u_300         276 non-null    float64
 52  p00u_300         276 non-null    float64
 53  p10u_300         276 non-null    float64
 54  p90r_300         276 non-null    float64
 55  p00r_300         276 non-null    float64
 56  p10r_300         276 non-null    int64  
 57  Country_encoded  276 non-null    int32  
dtypes: float64(49), int32(1), int64(7), object(1)
memory usage: 124.1+ KB
In [9]:
df.describe()
Out[9]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
count 276.000000 276.000000 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 ... 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 276.000000
mean 41.464873 -0.833154 2.634335e+08 2.816791e+08 2.987191e+08 1.748656e+08 1.861499e+08 1.968991e+08 8.856783e+07 9.552691e+07 ... 3.052863e+07 3.270072e+07 3.456480e+07 2.238403e+07 2.387204e+07 2.517659e+07 8.144602e+06 8.828685e+06 9.388209e+06 21.347826
std 13.138551 75.441222 1.640597e+08 1.833881e+08 1.994596e+08 9.068376e+07 9.689716e+07 1.015562e+08 8.880513e+07 1.033398e+08 ... 2.180212e+07 2.422163e+07 2.590455e+07 1.593904e+07 1.695498e+07 1.764387e+07 8.890845e+06 1.066851e+07 1.208475e+07 11.002910
min -33.968002 -124.209044 3.558760e+05 3.260370e+05 3.034580e+05 2.171250e+04 1.592310e+04 1.545940e+04 3.341640e+05 3.101140e+05 ... 3.586390e+04 3.641960e+04 3.453570e+04 7.970990e-01 7.348840e-01 6.874290e-01 3.586310e+04 3.641890e+04 3.453500e+04 0.000000
25% 35.735645 -77.396385 1.463155e+08 1.530615e+08 1.626702e+08 1.122075e+08 1.157828e+08 1.212025e+08 2.801438e+07 3.016952e+07 ... 1.344860e+07 1.439000e+07 1.516002e+07 8.849315e+06 1.001171e+07 1.033928e+07 2.499460e+06 2.590068e+06 2.853270e+06 10.000000
50% 42.839200 4.720832 2.033275e+08 2.145620e+08 2.195720e+08 1.474715e+08 1.564245e+08 1.678680e+08 6.151855e+07 6.502695e+07 ... 2.455375e+07 2.584920e+07 2.758905e+07 1.755090e+07 1.924650e+07 2.069725e+07 5.178405e+06 5.339695e+06 5.597290e+06 23.000000
75% 49.579176 30.497974 3.788732e+08 3.913535e+08 4.031740e+08 2.547372e+08 2.631975e+08 2.724995e+08 1.192925e+08 1.215025e+08 ... 4.479865e+07 4.662135e+07 4.966498e+07 3.533808e+07 3.770638e+07 3.972580e+07 1.017880e+07 1.043062e+07 1.063500e+07 33.000000
max 68.050658 166.539698 8.268430e+08 1.007240e+09 1.188970e+09 4.857500e+08 5.464730e+08 5.824590e+08 6.008490e+08 7.324380e+08 ... 1.154820e+08 1.531130e+08 1.784110e+08 6.816160e+07 7.082890e+07 7.296970e+07 7.945180e+07 1.031770e+08 1.202680e+08 33.000000

8 rows × 57 columns

In [10]:
z_scores = np.abs((df - np.mean(df,axis=0)) / np.std(df))
threshold = 3  # Adjust the threshold as needed

outliers_count = np.sum(z_scores > threshold)

print("Number of outliers:", outliers_count)
Number of outliers: Country            0
Country_encoded    0
Latitude           4
Longitude          0
p00_1200           4
p00_150            5
p00_30             8
p00_300            3
p00_600            4
p00_75             5
p00r_1200          4
p00r_150           3
p00r_30            4
p00r_300           5
p00r_600           6
p00r_75            4
p00u_1200          4
p00u_150           6
p00u_30            5
p00u_300           0
p00u_600           2
p00u_75            5
p10_1200           5
p10_150            5
p10_30             8
p10_300            3
p10_600            4
p10_75             6
p10r_1200          4
p10r_150           4
p10r_30            4
p10r_300           5
p10r_600           7
p10r_75            4
p10u_1200          4
p10u_150           7
p10u_30            5
p10u_300           0
p10u_600           2
p10u_75            5
p90_1200           4
p90_150            5
p90_30             6
p90_300            2
p90_600            3
p90_75             4
p90r_1200          4
p90r_150           3
p90r_30            5
p90r_300           5
p90r_600           5
p90r_75            3
p90u_1200          2
p90u_150           3
p90u_30            5
p90u_300           0
p90u_600           2
p90u_75            4
dtype: int64
C:\Users\prabha\Documents\Anaconda\lib\site-packages\numpy\core\fromnumeric.py:3430: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
  return mean(axis=axis, dtype=dtype, out=out, **kwargs)
C:\Users\prabha\Documents\Anaconda\lib\site-packages\numpy\core\fromnumeric.py:3571: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
In [11]:
num_cols = [
    'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200',
    'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30',
    'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75',
    'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150',
    'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600',
    'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300'
]

for col in num_cols:
    # Calculate z-scores
    z_scores = stats.zscore(df[col])

    # Set the threshold for z-score outliers
    threshold = 3

    # Detect outliers
    outliers = df[abs(z_scores) > threshold]

    # Visualize outliers using box plots
    plt.figure(figsize=(8, 6))
    sns.boxplot(x=df[col])
    plt.title(f"Boxplot of {col}")
    plt.show()

    # Print outlier details
    print(f"Outliers in {col}:")
    print(outliers)
Outliers in p90_1200:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p00_1200:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p10_1200:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p90u_1200:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
201  37271400.0  39811200                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p00u_1200:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
214   CHINA  29.101111  121.641944  722862000  811896000  865502000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
214  441405000.0  496909000.0  529558000.0  281457000  ...   60464400.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
214   68482800.0   73153500.0  40739400.0  46073000.0  49217400.0  19725000.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
201  37271400.0  39811200                6  
214  22409800.0  23936100                6  
243  47354800.0  50583400                6  

[4 rows x 58 columns]
Outliers in p10u_1200:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
214   CHINA  29.101111  121.641944  722862000  811896000  865502000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
214  441405000.0  496909000.0  529558000.0  281457000  ...   60464400.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
214   68482800.0   73153500.0  40739400.0  46073000.0  49217400.0  19725000.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
201  37271400.0  39811200                6  
214  22409800.0  23936100                6  
243  47354800.0  50583400                6  

[4 rows x 58 columns]
Outliers in p90r_1200:
    Country   Latitude  Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525  73.350193  658974000   816024000   958281000   
167   INDIA  28.159867  78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911  75.620019  780956000   957012000  1128870000   
239   INDIA  19.828785  72.661201  568977000   700672000   820686000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
239  184165000.0  230100000.0  269807000.0  384812000  ...   53975200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
239   69925300.0   81418700.0  28747300.0  37171700.0  43307300.0  25227900.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
239   32753500.0   38111400               12  

[4 rows x 58 columns]
Outliers in p00r_1200:
    Country   Latitude  Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525  73.350193  658974000   816024000   958281000   
167   INDIA  28.159867  78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911  75.620019  780956000   957012000  1128870000   
239   INDIA  19.828785  72.661201  568977000   700672000   820686000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
239  184165000.0  230100000.0  269807000.0  384812000  ...   53975200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
239   69925300.0   81418700.0  28747300.0  37171700.0  43307300.0  25227900.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
239   32753500.0   38111400               12  

[4 rows x 58 columns]
Outliers in p10r_1200:
    Country   Latitude  Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525  73.350193  658974000   816024000   958281000   
167   INDIA  28.159867  78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911  75.620019  780956000   957012000  1128870000   
239   INDIA  19.828785  72.661201  568977000   700672000   820686000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
239  184165000.0  230100000.0  269807000.0  384812000  ...   53975200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
239   69925300.0   81418700.0  28747300.0  37171700.0  43307300.0  25227900.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
239   32753500.0   38111400               12  

[4 rows x 58 columns]
Outliers in p90_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
39                CHINA  39.740929  116.030139  701332000  777851000   
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
45    6057090.0   7056470.0   7582200               30  
125   8113520.0  10397900.0  12891800               21  
132   8596700.0   8421880.0   8680890               17  
137   5411170.0   6294880.0   6769010               30  
260   1572500.0   1455070.0   1413740               23  

[6 rows x 58 columns]
Outliers in p00_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
39                CHINA  39.740929  116.030139  701332000  777851000   
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
97                CHINA  22.597144  114.543139  615931000  702252000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
147               CHINA  22.606110  114.553247  616314000  702669000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
45    6057090.0   7056470.0   7582200               30  
97   19904500.0  26459600.0  28317000                6  
125   8113520.0  10397900.0  12891800               21  
132   8596700.0   8421880.0   8680890               17  
137   5411170.0   6294880.0   6769010               30  
147  19925000.0  26481800.0  28340700                6  
260   1572500.0   1455070.0   1413740               23  

[8 rows x 58 columns]
Outliers in p10_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
39                CHINA  39.740929  116.030139  701332000  777851000   
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
97                CHINA  22.597144  114.543139  615931000  702252000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
147               CHINA  22.606110  114.553247  616314000  702669000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
45    6057090.0   7056470.0   7582200               30  
97   19904500.0  26459600.0  28317000                6  
125   8113520.0  10397900.0  12891800               21  
132   8596700.0   8421880.0   8680890               17  
137   5411170.0   6294880.0   6769010               30  
147  19925000.0  26481800.0  28340700                6  
260   1572500.0   1455070.0   1413740               23  

[8 rows x 58 columns]
Outliers in p90u_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

      p90r_300    p00r_300  p10r_300  Country_encoded  
45   6057090.0   7056470.0   7582200               30  
125  8113520.0  10397900.0  12891800               21  
132  8596700.0   8421880.0   8680890               17  
137  5411170.0   6294880.0   6769010               30  
260  1572500.0   1455070.0   1413740               23  

[5 rows x 58 columns]
Outliers in p00u_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

      p90r_300    p00r_300  p10r_300  Country_encoded  
45   6057090.0   7056470.0   7582200               30  
125  8113520.0  10397900.0  12891800               21  
132  8596700.0   8421880.0   8680890               17  
137  5411170.0   6294880.0   6769010               30  
260  1572500.0   1455070.0   1413740               23  

[5 rows x 58 columns]
Outliers in p10u_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

      p90r_300    p00r_300  p10r_300  Country_encoded  
45   6057090.0   7056470.0   7582200               30  
125  8113520.0  10397900.0  12891800               21  
132  8596700.0   8421880.0   8680890               17  
137  5411170.0   6294880.0   6769010               30  
260  1572500.0   1455070.0   1413740               23  

[5 rows x 58 columns]
Outliers in p90r_30:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[5 rows x 58 columns]
Outliers in p00r_30:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  

[4 rows x 58 columns]
Outliers in p10r_30:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  

[4 rows x 58 columns]
Outliers in p90_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
39                      CHINA  39.740929  116.030139  701332000  777851000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
147  19925000.0  26481800.0  28340700                6  

[4 rows x 58 columns]
Outliers in p00_75:
                      Country   Latitude   Longitude   p90_1200    p00_1200  \
39                      CHINA  39.740929  116.030139  701332000   777851000   
97                      CHINA  22.597144  114.543139  615931000   702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000   148816000   
147                     CHINA  22.606110  114.553247  616314000   702669000   
167                     INDIA  28.159867   78.408658  818999000  1007240000   

       p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39    829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97    758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118   164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
147   759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
167  1188970000  218150000.0  274801000.0  324681000.0  600849000  ...   

         p90_300      p00_300      p10_300    p90u_300    p00u_300  \
39    81160100.0   91306200.0   97528200.0  47571700.0  53381900.0   
97    64757000.0   85134500.0   91288100.0  44852500.0  58674900.0   
118   43882200.0   45617300.0   50369700.0  39721900.0  41334900.0   
147   64798900.0   85178800.0   91335400.0  44873900.0  58697000.0   
167  115482000.0  153113000.0  178411000.0  36030100.0  49936000.0   

       p10u_300    p90r_300     p00r_300   p10r_300  Country_encoded  
39   57023100.0  33588400.0   37924300.0   40505100                6  
97   62971100.0  19904500.0   26459600.0   28317000                6  
118  45655000.0   4160310.0    4282350.0    4714700               33  
147  62994700.0  19925000.0   26481800.0   28340700                6  
167  58143700.0  79451800.0  103177000.0  120268000               12  

[5 rows x 58 columns]
Outliers in p10_75:
                      Country   Latitude   Longitude   p90_1200    p00_1200  \
39                      CHINA  39.740929  116.030139  701332000   777851000   
97                      CHINA  22.597144  114.543139  615931000   702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000   148816000   
125                  PAKISTAN  24.845343   66.788517  362335000   468951000   
147                     CHINA  22.606110  114.553247  616314000   702669000   
167                     INDIA  28.159867   78.408658  818999000  1007240000   

       p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39    829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97    758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118   164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
125   562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
147   759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
167  1188970000  218150000.0  274801000.0  324681000.0  600849000  ...   

         p90_300      p00_300      p10_300    p90u_300    p00u_300  \
39    81160100.0   91306200.0   97528200.0  47571700.0  53381900.0   
97    64757000.0   85134500.0   91288100.0  44852500.0  58674900.0   
118   43882200.0   45617300.0   50369700.0  39721900.0  41334900.0   
125   19337200.0   25170100.0   31379300.0  11223700.0  14772100.0   
147   64798900.0   85178800.0   91335400.0  44873900.0  58697000.0   
167  115482000.0  153113000.0  178411000.0  36030100.0  49936000.0   

       p10u_300    p90r_300     p00r_300   p10r_300  Country_encoded  
39   57023100.0  33588400.0   37924300.0   40505100                6  
97   62971100.0  19904500.0   26459600.0   28317000                6  
118  45655000.0   4160310.0    4282350.0    4714700               33  
125  18487500.0   8113520.0   10397900.0   12891800               21  
147  62994700.0  19925000.0   26481800.0   28340700                6  
167  58143700.0  79451800.0  103177000.0  120268000               12  

[6 rows x 58 columns]
Outliers in p90u_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
6                     GERMANY  50.903056    6.421111  377877000  387602000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
6    400550000  265023000.0  272376000.0  282369000.0  112854000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
6    71747700.0  75061700.0  76803300.0  55377400.0  57904900.0  59294500.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
6    16370200.0  17156800.0  17508800               10  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
147  19925000.0  26481800.0  28340700                6  

[4 rows x 58 columns]
Outliers in p00u_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
39                      CHINA  39.740929  116.030139  701332000  777851000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
125                  PAKISTAN  24.845343   66.788517  362335000  468951000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
125   8113520.0  10397900.0  12891800               21  
147  19925000.0  26481800.0  28340700                6  

[5 rows x 58 columns]
Outliers in p10u_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
39                      CHINA  39.740929  116.030139  701332000  777851000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
125                  PAKISTAN  24.845343   66.788517  362335000  468951000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
125   8113520.0  10397900.0  12891800               21  
147  19925000.0  26481800.0  28340700                6  

[5 rows x 58 columns]
Outliers in p90r_75:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[3 rows x 58 columns]
Outliers in p00r_75:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[4 rows x 58 columns]
Outliers in p10r_75:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[4 rows x 58 columns]
Outliers in p90_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
121   JAPAN  36.458461  140.606342  150088000   153878000   155325000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
121  116253000.0  119213000.0  120223000.0   33834900  ...   52772100.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
121    9550020.0    9570870               15  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[5 rows x 58 columns]
Outliers in p00_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
237   CHINA  21.917778  112.981944  575483000   653428000   707083000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...   61722200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
237   28948400.0   30977000                6  

[5 rows x 58 columns]
Outliers in p10_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
237   CHINA  21.917778  112.981944  575483000   653428000   707083000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...   61722200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
237   28948400.0   30977000                6  

[5 rows x 58 columns]
Outliers in p90u_150:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
121   JAPAN  36.458461  140.606342  150088000  153878000  155325000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
245   JAPAN  36.466624  140.609604  150073000  153861000  155307000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...     p90_300  \
121  116253000.0  119213000.0  120223000.0   33834900  ...  52772100.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...  93688800.0   
245  116229000.0  119186000.0  120195000.0   33843900  ...  52762100.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
245   54998500.0   55127000.0  43501900.0  45446600.0  45554200.0   9260260.0   

       p00r_300  p10r_300  Country_encoded  
121   9550020.0   9570870               15  
201  37271400.0  39811200                6  
245   9551980.0   9572840               15  

[3 rows x 58 columns]
Outliers in p00u_150:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
97    CHINA  22.597144  114.543139  615931000  702252000  758819000   
121   JAPAN  36.458461  140.606342  150088000  153878000  155325000   
147   CHINA  22.606110  114.553247  616314000  702669000  759257000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
237   CHINA  21.917778  112.981944  575483000  653428000  707083000   
245   JAPAN  36.466624  140.609604  150073000  153861000  155307000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...     p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...  64757000.0   
121  116253000.0  119213000.0  120223000.0   33834900  ...  52772100.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...  64798900.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...  93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...  61722200.0   
245  116229000.0  119186000.0  120195000.0   33843900  ...  52762100.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   
245   54998500.0   55127000.0  43501900.0  45446600.0  45554200.0   9260260.0   

       p00r_300  p10r_300  Country_encoded  
97   26459600.0  28317000                6  
121   9550020.0   9570870               15  
147  26481800.0  28340700                6  
201  37271400.0  39811200                6  
237  28948400.0  30977000                6  
245   9551980.0   9572840               15  

[6 rows x 58 columns]
Outliers in p10u_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
121   JAPAN  36.458461  140.606342  150088000   153878000   155325000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
237   CHINA  21.917778  112.981944  575483000   653428000   707083000   
245   JAPAN  36.466624  140.609604  150073000   153861000   155307000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
121  116253000.0  119213000.0  120223000.0   33834900  ...   52772100.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...   61722200.0   
245  116229000.0  119186000.0  120195000.0   33843900  ...   52762100.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   
245   54998500.0   55127000.0  43501900.0  45446600.0  45554200.0   9260260.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
121    9550020.0    9570870               15  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
237   28948400.0   30977000                6  
245    9551980.0    9572840               15  

[7 rows x 58 columns]
Outliers in p90r_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[3 rows x 58 columns]
Outliers in p00r_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  

[3 rows x 58 columns]
Outliers in p10r_150:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39      CHINA  39.740929  116.030139  701332000   777851000   829594000   
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  

[4 rows x 58 columns]
Outliers in p90_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
167  103177000.0  120268000               12  
243   47354800.0   50583400                6  

[3 rows x 58 columns]
Outliers in p00_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p10_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p90u_600:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p00u_600:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p10u_600:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p90r_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
153   INDIA  12.553056   80.173333  356322000   421981000   489751000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
153  115092000.0  137358000.0  159491000.0  241229000  ...   48114200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
153   55720800.0   64848800.0  19946200.0  23014700.0  26796200.0  28168000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
153   32706100.0   38052600               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p00r_600:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
153     INDIA  12.553056   80.173333  356322000   421981000   489751000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
153  115092000.0  137358000.0  159491000.0  241229000  ...   48114200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
153   55720800.0   64848800.0  19946200.0  23014700.0  26796200.0  28168000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
153   32706100.0   38052600               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[6 rows x 58 columns]
Outliers in p10r_600:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
122     INDIA  14.865026   74.438709  411477000   487089000   565595000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
153     INDIA  12.553056   80.173333  356322000   421981000   489751000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
122  137096000.0  163614000.0  190060000.0  274382000  ...   35497700.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
153  115092000.0  137358000.0  159491000.0  241229000  ...   48114200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
122   43529800.0   50642200.0   9658560.0  11757800.0  13680800.0  25839200.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
153   55720800.0   64848800.0  19946200.0  23014700.0  26796200.0  28168000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
122   31772000.0   36961400               12  
123   48695900.0   56664800               12  
153   32706100.0   38052600               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[7 rows x 58 columns]
Outliers in p90_300:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
243   47354800.0   50583400                6  

[2 rows x 58 columns]
Outliers in p00_300:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
243   47354800.0   50583400                6  

[3 rows x 58 columns]
Outliers in p10_300:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
243   47354800.0   50583400                6  

[3 rows x 58 columns]
Outliers in p90u_300:
Empty DataFrame
Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded]
Index: []

[0 rows x 58 columns]
Outliers in p00u_300:
Empty DataFrame
Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded]
Index: []

[0 rows x 58 columns]
Outliers in p10u_300:
Empty DataFrame
Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded]
Index: []

[0 rows x 58 columns]
Outliers in p90r_300:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p00r_300:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p10r_300:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
In [12]:
# Select the columns you want to standardize
column_standardize = ['Country_encoded', 'Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300']


# Create a StandardScaler object
scaler = StandardScaler()

# Fit the scaler to the selected columns
scaler.fit(df[column_standardize])

# Transform the selected columns by applying standardization
df[column_standardize] = scaler.transform(df[column_standardize])
In [13]:
df[column_standardize]
Out[13]:
Country_encoded Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p10r_600 p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300
0 0.605681 1.352765 0.251194 -0.464402 -0.508016 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 ... -0.599107 -1.172443 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829 -0.636833
1 0.514631 -0.126329 -0.064588 -0.774040 -0.731805 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 ... -0.317110 -0.586885 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095 -0.156917
2 -1.670572 -4.916056 -0.579314 -1.002907 -0.916580 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 ... -0.339928 0.414370 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894 -0.068467
3 1.060932 -0.469286 -1.226995 -0.889115 -0.813688 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 ... -0.584963 -1.145335 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664 -0.593155
4 0.514631 -0.020197 0.018589 0.051419 0.029799 0.051246 0.176309 0.148003 0.179467 -0.085040 ... -0.367127 -0.741186 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095 -0.473250
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 -1.397422 -1.506439 1.501828 1.792834 1.899641 1.913771 0.839405 1.051898 1.110167 2.454943 ... 1.483419 1.727114 2.251499 2.276462 1.196198 1.746700 1.828051 2.090747 2.335818 2.210780
272 1.060932 0.096301 -0.957393 -0.854920 -0.820254 -0.771264 -0.789761 -0.786267 -0.743154 -0.772925 ... -0.572764 0.245594 0.188036 0.253678 0.620326 0.569365 0.671899 -0.509843 -0.477951 -0.437206
273 -0.395870 -0.461599 1.689799 2.357024 2.340021 2.270520 2.538004 2.634711 2.644594 1.762702 ... 0.120417 0.566912 0.570582 0.541617 0.867854 0.941671 0.931923 -0.165667 -0.201114 -0.199622
274 0.878832 0.461080 0.470339 0.061055 -0.037393 -0.131383 -0.141744 -0.234249 -0.340844 0.257537 ... -0.223553 -0.455104 -0.551699 -0.640980 -0.537094 -0.643647 -0.744355 -0.153133 -0.229645 -0.287221
275 1.060932 0.074844 -1.154910 -0.616189 -0.577300 -0.524737 -0.479518 -0.452401 -0.391292 -0.648693 ... -0.535978 -0.497452 -0.464913 -0.420298 -0.360175 -0.330085 -0.282576 -0.574150 -0.530945 -0.488370

276 rows × 57 columns

In [14]:
#correlation 
df.corr()  
C:\Users\prabha\AppData\Local\Temp\ipykernel_18132\930112212.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  df.corr()
Out[14]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
Latitude 1.000000 -0.042836 -0.009781 -0.073953 -0.111928 0.156477 0.090043 0.053836 -0.177858 -0.215616 ... 0.012607 -0.045970 -0.078337 0.082410 0.031985 0.005220 -0.116827 -0.155200 -0.175544 0.084602
Longitude -0.042836 1.000000 0.593553 0.587779 0.567162 0.503450 0.514429 0.498042 0.582437 0.560700 ... 0.448198 0.446263 0.426163 0.354557 0.361703 0.341938 0.463441 0.438351 0.414278 -0.549427
p90_1200 -0.009781 0.593553 1.000000 0.993697 0.981456 0.915903 0.940142 0.947505 0.912134 0.881890 ... 0.683437 0.705045 0.703963 0.503745 0.537621 0.541605 0.772836 0.746307 0.718247 -0.552493
p00_1200 -0.073953 0.587779 0.993697 1.000000 0.996458 0.874553 0.909984 0.924483 0.942714 0.921350 ... 0.671828 0.705215 0.712202 0.476288 0.518611 0.528324 0.793592 0.776905 0.755299 -0.529692
p10_1200 -0.111928 0.567162 0.981456 0.996458 1.000000 0.838057 0.880295 0.900355 0.957368 0.942899 ... 0.662495 0.703799 0.717241 0.455220 0.502707 0.516957 0.808476 0.798964 0.782695 -0.510042
p90u_1200 0.156477 0.503450 0.915903 0.874553 0.838057 1.000000 0.994082 0.984650 0.670895 0.619903 ... 0.655884 0.636550 0.611634 0.584574 0.589668 0.579526 0.560367 0.508081 0.464968 -0.518746
p00u_1200 0.090043 0.514429 0.940142 0.909984 0.880295 0.994082 1.000000 0.997322 0.721719 0.677228 ... 0.660659 0.652715 0.634439 0.573294 0.587676 0.582786 0.592297 0.547947 0.509093 -0.510720
p10u_1200 0.053836 0.498042 0.947505 0.924483 0.900355 0.984650 0.997322 1.000000 0.744952 0.705466 ... 0.662244 0.661272 0.648351 0.565323 0.585009 0.584451 0.610473 0.571614 0.536482 -0.499197
p90r_1200 -0.177858 0.582437 0.912134 0.942714 0.957368 0.670895 0.721719 0.744952 1.000000 0.996198 ... 0.592830 0.652492 0.675936 0.333685 0.391065 0.408782 0.855526 0.859906 0.852093 -0.490963
p00r_1200 -0.215616 0.560700 0.881890 0.921350 0.942899 0.619903 0.677228 0.705466 0.996198 1.000000 ... 0.572753 0.639449 0.668980 0.307685 0.369304 0.391125 0.852904 0.864876 0.862962 -0.461099
p10r_1200 -0.238073 0.535896 0.852854 0.898521 0.925566 0.575487 0.636644 0.668593 0.987911 0.997540 ... 0.555958 0.627389 0.661602 0.286079 0.350105 0.374943 0.850451 0.868009 0.870772 -0.437276
p90_30 -0.042807 0.278141 0.390220 0.398820 0.397571 0.361934 0.383573 0.390973 0.351306 0.348093 ... 0.342201 0.353131 0.350017 0.314624 0.334661 0.337283 0.275103 0.269882 0.257849 -0.156847
p00_30 -0.071382 0.272715 0.395847 0.410268 0.413002 0.349772 0.376796 0.387706 0.374122 0.374761 ... 0.337559 0.356699 0.357316 0.300235 0.327166 0.332768 0.289517 0.289894 0.280089 -0.153498
p10_30 -0.091969 0.261762 0.385404 0.404440 0.410763 0.327720 0.358283 0.371956 0.377348 0.381776 ... 0.319007 0.342038 0.346256 0.277772 0.307134 0.315436 0.284293 0.288444 0.281686 -0.139111
p90u_30 -0.025076 0.202648 0.262994 0.269027 0.266086 0.263968 0.280668 0.285346 0.216306 0.214256 ... 0.211601 0.212181 0.206587 0.225596 0.235222 0.235816 0.114451 0.107904 0.098541 -0.073771
p00u_30 -0.048304 0.197067 0.265332 0.275863 0.275960 0.253946 0.274822 0.282248 0.230859 0.231867 ... 0.204362 0.210474 0.207392 0.212824 0.227324 0.230066 0.119596 0.116580 0.108661 -0.070419
p10u_30 -0.065892 0.187799 0.255588 0.269769 0.272688 0.235632 0.259306 0.268926 0.231560 0.235601 ... 0.187099 0.196002 0.195585 0.193482 0.209781 0.214621 0.111940 0.111605 0.105901 -0.059179
p90r_30 -0.091113 0.421084 0.682476 0.696501 0.703346 0.546776 0.575516 0.589791 0.702471 0.696355 ... 0.680388 0.728709 0.738298 0.491953 0.543683 0.553320 0.786499 0.790399 0.774745 -0.411759
p00r_30 -0.117985 0.400524 0.664460 0.685194 0.696747 0.508891 0.543169 0.561031 0.707875 0.706620 ... 0.654482 0.713562 0.728623 0.457503 0.516960 0.530431 0.784732 0.798480 0.787424 -0.387928
p10r_30 -0.136154 0.386479 0.653287 0.679280 0.695131 0.481798 0.519890 0.540839 0.714900 0.717950 ... 0.639250 0.703954 0.724050 0.433494 0.496047 0.512797 0.790424 0.809901 0.803366 -0.369033
p90_75 -0.089833 0.290413 0.494629 0.504149 0.505120 0.440615 0.466112 0.477876 0.463846 0.457601 ... 0.588741 0.613657 0.613717 0.545132 0.584054 0.594746 0.466427 0.465026 0.447212 -0.257120
p00_75 -0.125558 0.282451 0.497924 0.514842 0.520680 0.419051 0.451512 0.467409 0.491956 0.490267 ... 0.568476 0.605787 0.610878 0.508644 0.557429 0.571629 0.482146 0.489473 0.474878 -0.250862
p10_75 -0.149335 0.269202 0.490433 0.512592 0.522592 0.396990 0.433616 0.452825 0.500642 0.503052 ... 0.552840 0.595212 0.604845 0.485971 0.537956 0.555543 0.484449 0.496410 0.485430 -0.233543
p90u_75 -0.069933 0.193839 0.348977 0.355197 0.353557 0.335294 0.355842 0.364899 0.302318 0.296675 ... 0.449885 0.464933 0.461427 0.469045 0.499679 0.509164 0.262329 0.261460 0.245716 -0.159453
p00u_75 -0.101832 0.189903 0.354118 0.366118 0.368009 0.321228 0.347575 0.359835 0.326178 0.323806 ... 0.431643 0.456767 0.456650 0.438503 0.477678 0.489641 0.272349 0.277886 0.263982 -0.158670
p10u_75 -0.122215 0.177409 0.345034 0.361059 0.366094 0.302142 0.331730 0.346565 0.328885 0.329683 ... 0.414566 0.443150 0.446317 0.418013 0.459435 0.474019 0.267207 0.275960 0.264639 -0.142690
p90r_75 -0.107191 0.448397 0.703960 0.719068 0.727788 0.549890 0.578077 0.592412 0.738981 0.733992 ... 0.728898 0.772337 0.783635 0.509052 0.554405 0.563481 0.874802 0.872412 0.857091 -0.434870
p00r_75 -0.134632 0.421512 0.684287 0.707621 0.722479 0.505640 0.540367 0.559310 0.747822 0.749038 ... 0.698500 0.754025 0.772329 0.466688 0.520151 0.534016 0.876208 0.885273 0.875878 -0.404121
p10r_75 -0.152810 0.402159 0.668005 0.696889 0.716396 0.473309 0.511908 0.534159 0.750758 0.756674 ... 0.677438 0.738747 0.762515 0.437371 0.493851 0.511275 0.877116 0.892386 0.888040 -0.381498
p90_150 -0.078705 0.383489 0.498071 0.499282 0.496760 0.457713 0.471595 0.476610 0.452746 0.443829 ... 0.808004 0.813821 0.805575 0.773159 0.796794 0.800752 0.595307 0.581380 0.557702 -0.358007
p00_150 -0.121544 0.375054 0.520748 0.531776 0.536025 0.443795 0.467185 0.478155 0.508852 0.505629 ... 0.796680 0.820111 0.819651 0.733034 0.770332 0.779727 0.639474 0.637715 0.618573 -0.352651
p10_150 -0.147272 0.355924 0.522420 0.539637 0.548932 0.426676 0.455277 0.470538 0.529421 0.530741 ... 0.786982 0.817871 0.823582 0.710949 0.753224 0.767324 0.655283 0.659817 0.645107 -0.334995
p90u_150 -0.050610 0.295709 0.328158 0.322101 0.313866 0.351819 0.356897 0.356464 0.246981 0.236963 ... 0.691987 0.679992 0.663645 0.746001 0.755851 0.756108 0.359498 0.342604 0.318644 -0.256568
p00u_150 -0.089724 0.294465 0.357470 0.359223 0.356044 0.351899 0.365227 0.369677 0.301051 0.295030 ... 0.695414 0.698221 0.687508 0.726595 0.748819 0.753435 0.402693 0.395166 0.373700 -0.260056
p10u_150 -0.112548 0.275048 0.359229 0.365703 0.366302 0.341098 0.358716 0.366706 0.315330 0.312632 ... 0.689454 0.697963 0.691899 0.714137 0.740600 0.749300 0.410411 0.407643 0.389150 -0.244000
p90r_150 -0.117169 0.445536 0.721368 0.740618 0.753745 0.534640 0.565129 0.581931 0.786713 0.784373 ... 0.762769 0.811600 0.827516 0.515817 0.564655 0.576388 0.945732 0.945264 0.932310 -0.465686
p00r_150 -0.147858 0.416005 0.695123 0.723150 0.742765 0.484330 0.521791 0.543482 0.789603 0.794011 ... 0.723758 0.785402 0.808567 0.467564 0.525080 0.541814 0.936575 0.948680 0.942170 -0.429625
p10r_150 -0.165813 0.394212 0.673938 0.707648 0.732051 0.448201 0.489486 0.514540 0.787358 0.796789 ... 0.697431 0.764559 0.793337 0.434420 0.494539 0.514863 0.931435 0.949895 0.948872 -0.402909
p90_600 0.076954 0.445624 0.843624 0.820751 0.804664 0.822802 0.822563 0.822873 0.718312 0.685215 ... 0.865318 0.854707 0.841665 0.735396 0.745268 0.742552 0.803555 0.756093 0.720038 -0.559132
p00_600 0.014709 0.451663 0.863248 0.852601 0.844762 0.802299 0.813779 0.821253 0.775502 0.749968 ... 0.863481 0.867144 0.862588 0.709083 0.729632 0.732838 0.846222 0.809181 0.779070 -0.551341
p10_600 -0.020343 0.433592 0.859790 0.857548 0.856500 0.774521 0.792916 0.805978 0.797480 0.778304 ... 0.854128 0.866162 0.868737 0.685258 0.711227 0.719475 0.865996 0.836200 0.811760 -0.532534
p90u_600 0.198119 0.342015 0.681404 0.633832 0.600762 0.801413 0.777294 0.764457 0.440467 0.395981 ... 0.828019 0.782108 0.748807 0.823624 0.808715 0.795026 0.553918 0.490431 0.444374 -0.485486
p00u_600 0.147573 0.354976 0.718142 0.678759 0.650654 0.812509 0.797710 0.790220 0.497006 0.456562 ... 0.845781 0.810137 0.782014 0.824353 0.818288 0.808881 0.596168 0.538855 0.495329 -0.491781
p10u_600 0.121725 0.335228 0.724191 0.690346 0.666795 0.805892 0.796343 0.793384 0.514937 0.478404 ... 0.848104 0.817994 0.794808 0.819094 0.817201 0.812011 0.611291 0.558423 0.518183 -0.479169
p90r_600 -0.116411 0.474364 0.849645 0.868109 0.880540 0.633530 0.667412 0.686391 0.922714 0.914701 ... 0.687381 0.729949 0.749285 0.413130 0.455710 0.469367 0.944956 0.933026 0.920867 -0.514817
p00r_600 -0.158411 0.458346 0.825450 0.854177 0.874190 0.582275 0.624287 0.648904 0.930355 0.930413 ... 0.663231 0.716049 0.742704 0.379200 0.428367 0.446821 0.946566 0.944921 0.939678 -0.484983
p10r_600 -0.182044 0.437295 0.797284 0.832764 0.858436 0.537779 0.584269 0.612792 0.923757 0.929932 ... 0.640775 0.699237 0.731425 0.351110 0.403125 0.425036 0.941858 0.946870 0.947306 -0.457815
p90_300 0.012607 0.448198 0.683437 0.671828 0.662495 0.655884 0.660659 0.662244 0.592830 0.572753 ... 1.000000 0.990770 0.977804 0.935726 0.950012 0.950329 0.774680 0.739618 0.708504 -0.456634
p00_300 -0.045970 0.446263 0.705045 0.705215 0.703799 0.636550 0.652715 0.661272 0.652492 0.639449 ... 0.990770 1.000000 0.996047 0.897112 0.925717 0.932376 0.821271 0.799186 0.773820 -0.451601
p10_300 -0.078337 0.426163 0.703963 0.712202 0.717241 0.611634 0.634439 0.648351 0.675936 0.668980 ... 0.977804 0.996047 1.000000 0.868113 0.902519 0.914888 0.841464 0.827076 0.807827 -0.430609
p90u_300 0.082410 0.354557 0.503745 0.476288 0.455220 0.584574 0.573294 0.565323 0.333685 0.307685 ... 0.935726 0.897112 0.868113 1.000000 0.993769 0.986514 0.501838 0.457438 0.420541 -0.353348
p00u_300 0.031985 0.361703 0.537621 0.518611 0.502707 0.589668 0.587676 0.585009 0.391065 0.369304 ... 0.950012 0.925717 0.902519 0.993769 1.000000 0.997512 0.548043 0.512479 0.478237 -0.360308
p10u_300 0.005220 0.341938 0.541605 0.528324 0.516957 0.579526 0.582786 0.584451 0.408782 0.391125 ... 0.950329 0.932376 0.914888 0.986514 0.997512 1.000000 0.561826 0.531552 0.501118 -0.342843
p90r_300 -0.116827 0.463441 0.772836 0.793592 0.808476 0.560367 0.592297 0.610473 0.855526 0.852904 ... 0.774680 0.821271 0.841464 0.501838 0.548043 0.561826 1.000000 0.993620 0.983469 -0.486294
p00r_300 -0.155200 0.438351 0.746307 0.776905 0.798964 0.508081 0.547947 0.571614 0.859906 0.864876 ... 0.739618 0.799186 0.827076 0.457438 0.512479 0.531552 0.993620 1.000000 0.996828 -0.452686
p10r_300 -0.175544 0.414278 0.718247 0.755299 0.782695 0.464968 0.509093 0.536482 0.852093 0.862962 ... 0.708504 0.773820 0.807827 0.420541 0.478237 0.501118 0.983469 0.996828 1.000000 -0.422487
Country_encoded 0.084602 -0.549427 -0.552493 -0.529692 -0.510042 -0.518746 -0.510720 -0.499197 -0.490963 -0.461099 ... -0.456634 -0.451601 -0.430609 -0.353348 -0.360308 -0.342843 -0.486294 -0.452686 -0.422487 1.000000

57 rows × 57 columns

In [15]:
# Step 2: Subset the data for relevant features
independent_features = ['p90_1200', 'p90u_1200', 'p90r_1200', 'p90_30', 'p90u_30', 'p90r_30', 'p90_75',
                        'p90u_75', 'p90r_75', 'p90_150', 'p90u_150', 'p90r_150', 'p90_600', 'p90u_600',
                        'p90r_600', 'p90_300', 'p90u_300', 'p90r_300']
dependent_features = ['p00_1200', 'p10_1200', 'p00u_1200', 'p10u_1200', 'p00r_1200', 'p10r_1200', 'p00_30',
                      'p10_30', 'p00u_30', 'p10u_30', 'p00r_30', 'p10r_30', 'p00_75', 'p10_75', 'p00u_75',
                      'p10u_75', 'p00r_75', 'p10r_75', 'p00_150', 'p10_150', 'p00u_150', 'p10u_150', 'p00r_150',
                      'p10r_150', 'p00_600', 'p10_600', 'p00u_600', 'p10u_600', 'p00r_600', 'p10r_600', 'p00_300',
                      'p10_300', 'p00u_300', 'p10u_300', 'p00r_300', 'p10r_300']

relevant_columns = independent_features + dependent_features
relevant_data = df[relevant_columns]

# Step 3: Data visualization
# Example 1: Box plot comparing independent features
plt.figure(figsize=(20, 12))
sns.boxplot(data=relevant_data[independent_features])
plt.title('Population Distribution - Independent Features')
plt.xticks(rotation=90)
plt.show()
In [16]:
# Compute the correlation matrix
correlation_matrix = df.corr()

# Set the dark background style
sns.set_style('dark')

# Create the heat map
plt.figure(figsize=(40,32))
sns.heatmap(correlation_matrix, cmap='coolwarm', annot=True, fmt='.2f')
plt.title('Correlation Heat Map')
plt.show()
C:\Users\prabha\AppData\Local\Temp\ipykernel_18132\3568341587.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  correlation_matrix = df.corr()
In [17]:
import pandas as pd

# Print the first few rows of the DataFrame
print(df.head())

# List the column names of the DataFrame
print("Column names:", df.columns)

# Check the data types of the columns
print("Data types:", df.dtypes)

# Find the unique values in each column
for column in df.columns:
    unique_values = df[column].unique()
    print(f"Unique values in {column}:", unique_values)
                    Country  Latitude  Longitude  p90_1200  p00_1200  \
0                    SWEDEN  1.352765   0.251194 -0.464402 -0.508016   
1                     SPAIN -0.126329  -0.064588 -0.774040 -0.731805   
2                    BRAZIL -4.916056  -0.579314 -1.002907 -0.916580   
3  UNITED STATES OF AMERICA -0.469286  -1.226995 -0.889115 -0.813688   
4                     SPAIN -0.020197   0.018589  0.051419  0.029799   

   p10_1200  p90u_1200  p00u_1200  p10u_1200  p90r_1200  ...   p90_300  \
0 -0.554848  -0.593107  -0.660152  -0.734917  -0.252291  ... -1.172443   
1 -0.679515  -0.964721  -0.953322  -0.919337  -0.444835  ... -0.586885   
2 -0.857975  -1.183329  -1.120005  -1.079853  -0.644420  ...  0.414370   
3 -0.764634  -0.908713  -0.841270  -0.801030  -0.714629  ... -1.145335   
4  0.051246   0.176309   0.148003   0.179467  -0.085040  ... -0.741186   

    p00_300   p10_300  p90u_300  p00u_300  p10u_300  p90r_300  p00r_300  \
0 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829   
1 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095   
2  0.496365  0.605076  0.653952  0.779507  0.935261 -0.156248 -0.111894   
3 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664   
4 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095   

   p10r_300  Country_encoded  
0 -0.636833         0.605681  
1 -0.156917         0.514631  
2 -0.068467        -1.670572  
3 -0.593155         1.060932  
4 -0.473250         0.514631  

[5 rows x 58 columns]
Column names: Index(['Country', 'Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200',
       'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200',
       'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30',
       'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75',
       'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75',
       'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150',
       'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600',
       'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600',
       'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300',
       'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded'],
      dtype='object')
Data types: Country             object
Latitude           float64
Longitude          float64
p90_1200           float64
p00_1200           float64
p10_1200           float64
p90u_1200          float64
p00u_1200          float64
p10u_1200          float64
p90r_1200          float64
p00r_1200          float64
p10r_1200          float64
p90_30             float64
p00_30             float64
p10_30             float64
p90u_30            float64
p00u_30            float64
p10u_30            float64
p90r_30            float64
p00r_30            float64
p10r_30            float64
p90_75             float64
p00_75             float64
p10_75             float64
p90u_75            float64
p00u_75            float64
p10u_75            float64
p90r_75            float64
p00r_75            float64
p10r_75            float64
p90_150            float64
p00_150            float64
p10_150            float64
p90u_150           float64
p00u_150           float64
p10u_150           float64
p90r_150           float64
p00r_150           float64
p10r_150           float64
p90_600            float64
p00_600            float64
p10_600            float64
p90u_600           float64
p00u_600           float64
p10u_600           float64
p90r_600           float64
p00r_600           float64
p10r_600           float64
p90_300            float64
p00_300            float64
p10_300            float64
p90u_300           float64
p00u_300           float64
p10u_300           float64
p90r_300           float64
p00r_300           float64
p10r_300           float64
Country_encoded    float64
dtype: object
Unique values in Country: ['SWEDEN' 'SPAIN' 'BRAZIL' 'UNITED STATES OF AMERICA' 'ARGENTINA'
 'GERMANY' 'RUSSIAN FEDERATION' 'BULGARIA' 'FRANCE' 'UNITED KINGDOM'
 'SWITZERLAND' 'KAZAKHSTAN' 'SLOVAK REPUBLIC' 'NETHERLANDS' 'CANADA'
 'IRAN, ISLAMIC REPUBLIC OF' 'ITALY' 'CHINA' 'ROMANIA' 'PAKISTAN'
 'UKRAINE' 'TAIWAN, CHINA' 'BELGIUM' 'CZECH REPUBLIC' 'JAPAN' 'LITHUANIA'
 'INDIA' 'SOUTH AFRICA' 'KOREA, REPUBLIC OF' 'SLOVENIA' 'MEXICO' 'FINLAND'
 'ARMENIA' 'HUNGARY']
Unique values in Latitude: [ 1.35276534e+00 -1.26329198e-01 -4.91605559e+00 -4.69285615e-01
 -2.01966081e-02 -5.75176834e+00  7.19662906e-01  8.10391363e-01
  1.08881949e+00 -6.42768222e-02  1.65048566e-01  4.60751238e-01
  1.17311830e+00  7.79858133e-01  4.64155655e-01  6.28639209e-01
  2.96919319e-01  2.02717020e+00  2.89066925e-01  1.63337513e-01
  5.36005870e-01 -1.76126296e+00  7.59966221e-01  7.83585927e-01
 -1.68721014e-02  5.25243619e-01  9.44431357e-01 -5.15502359e-01
  2.18171743e-01  9.47544955e-01 -5.72377348e-01  3.30218600e-01
 -9.63498625e-01  4.65222766e-02 -2.06376609e-01 -2.31051159e-01
  2.75048563e-01 -4.89028305e-01  6.06091657e-01 -1.31450997e-01
  2.17869259e-01 -1.68069194e+00  1.03324959e+00 -6.91889629e-01
  7.56758377e-01 -1.23362219e+00  4.39641307e-01  6.57667261e-01
  3.80576565e-01 -9.85893915e-02 -1.71423539e-01  3.81726417e-01
 -6.98990729e-01 -8.41018622e-02  3.27376985e-01  2.41448170e-01
 -9.53711994e-01 -5.49182052e-01  4.78014273e-01  1.83335488e-01
  1.00899530e-02 -4.76805405e-01  7.95658842e-01  7.51857399e-01
  3.89811364e-02  2.18212232e-01  1.30488924e+00 -5.73195393e-03
  4.84742806e-02  5.81178902e-01  7.20488924e-01  2.92060888e-01
 -5.61940966e+00  8.39465242e-01  3.80051344e-02 -1.51353315e+00
 -7.80959645e-01  4.91167425e-01  1.57001125e-01  6.15409426e-01
  1.44401893e+00  4.26444126e-03 -9.30579003e-02 -4.35421092e-01
 -3.08297140e-01 -3.16334745e-01 -1.22128476e+00 -1.57500804e-02
 -6.06049417e-01  3.75893204e-01  4.49945143e-01  2.01400064e-01
  6.49591302e-01 -7.21126149e-01  7.28282529e-01  9.66525067e-01
  8.05973811e-01 -1.43866724e+00  5.37702894e-01 -5.38293530e-01
  1.30166527e-03 -3.62686909e-01 -6.90538943e-02 -5.21855750e-01
  1.00421079e+00 -7.26715285e-01  6.55009562e-01  9.58087302e-01
 -2.11073400e-02  7.42971282e-01  7.42993776e-01 -1.26798593e-01
 -1.52277125e-01 -5.51708763e-02  1.08697271e+00  1.08712506e+00
  1.07814447e+00 -6.07987009e-01 -1.47523472e-02  5.44400097e-01
 -8.50727554e-02 -3.81739687e-01 -2.02824243e+00 -1.54241465e+00
  1.25358128e+00 -1.26724172e+00 -3.07549050e-01  2.19465251e-01
  6.73867521e-01  5.82179609e-01 -5.72951473e+00  1.98265178e+00
 -4.68450752e-01  1.73679092e-01  3.41075173e-01  9.10817772e-01
 -2.53882391e+00 -1.23999144e+00  7.78658870e-01  1.59758483e-01
 -1.65803177e+00 -1.68492264e-02 -3.01821095e-03  4.67904570e-01
  1.40156536e+00  1.40038189e+00 -9.44384094e-02 -1.43798358e+00
  8.40076463e-01  1.44156322e+00  3.98628560e-01 -1.25250501e+00
 -1.48738248e+00 -2.20453050e+00  1.89798070e-01  2.04232986e-01
 -4.59992246e-01 -9.76977222e-02 -4.39379542e-01 -1.19158414e-02
  5.18598418e-01  7.44090711e-01 -4.36501252e-01  2.94968153e-01
  4.19692742e-01  6.81919080e-01  5.82503443e-01 -1.01450876e+00
  5.77737961e-01  5.44407417e-01  1.56871500e-01 -1.09951754e+00
  5.37741019e-01 -2.59561092e-01  7.48656570e-01  7.48023694e-01
  1.03847326e+00  6.02343504e-01 -5.08830470e-01 -4.51691883e-01
  3.41974205e-03  7.76516165e-01  1.50757778e+00 -2.33697039e-01
  1.21618004e+00 -1.25864072e-01  3.89457344e-01  6.53713147e-02
 -6.15733186e-01  6.39984850e-01  1.63079101e-01 -1.30080705e-01
  6.48944243e-01  2.56373594e-02  5.93829870e-01  1.78849539e-01
  3.65640065e-02 -1.01607601e-01  2.14714866e-01  2.74791905e-01
  2.40533168e-01 -8.40977590e-01  1.99185979e-02  1.38224753e-01
 -1.26513966e+00 -2.37869600e-01  8.90763372e-01  1.20439162e+00
 -8.16461717e-01  3.60037519e-01  7.52071509e-01  4.76954396e-01
 -1.52658376e-01 -6.17212439e-01 -9.42738758e-01  9.98525493e-02
 -9.43922780e-02  1.09283778e-01  9.87868553e-01 -7.34390473e-01
 -4.75920904e-01 -4.44658341e-01 -3.35873008e-01 -4.51804276e-01
 -4.37914243e-01 -6.43369836e-02 -3.84119312e-02  8.19698838e-01
  9.68516264e-01 -9.66079769e-01  4.83982449e-01  3.00435289e-01
  4.76977271e-01 -1.07636799e+00  9.26830305e-01 -3.27820230e-01
 -2.84544998e-02 -1.49046901e+00 -4.53102054e-01 -1.64975504e+00
  5.87983237e-01 -1.00022894e-01  7.78841490e-01 -5.16778176e-01
  6.91474597e-01 -3.81117257e-01  1.19792575e-01  1.10589792e+00
  8.73826078e-01  2.18443499e-01 -5.82863051e-02  3.48727028e-01
 -4.35782823e-01 -1.22228029e+00 -3.33354084e-01  9.12167400e-01
  6.55312580e-01 -2.93685552e-01 -3.91832015e-02  1.00301510e-01
  1.40807469e+00 -5.46642922e-01 -6.34627974e-01  4.67754205e-01
 -8.74579584e-01 -4.46983970e-01  7.02807352e-01 -2.45980939e-01
 -4.38698400e-01  7.75791483e-01  9.11328648e-01 -1.50643922e+00
  9.63013509e-02 -4.61598837e-01  4.61079800e-01  7.48437188e-02]
Unique values in Longitude: [ 2.51194074e-01 -6.45883067e-02 -5.79314444e-01 -1.22699469e+00
  1.85888724e-02 -7.75128185e-01  9.63325198e-02  6.47902346e-01
  1.82645132e-01 -1.05705264e+00  3.45563995e-01  4.92753807e-02
  8.25314726e-01 -2.20499191e-02  1.20329302e-01  1.22806832e-01
 -1.12030684e+00  2.22261631e+00  1.87600418e-03  6.92072895e-01
  2.45869979e-01 -8.82225085e-01  6.04313646e-02  2.29747233e-02
 -1.16056580e+00 -4.03568370e-02  1.35156411e-01 -1.14582165e+00
 -1.07246765e+00  1.33256722e-01 -1.02487259e+00  8.10464202e-02
  6.86890690e-01 -1.17454905e+00 -1.20774497e+00 -1.00404115e+00
  1.42160222e-01 -1.06549172e+00  9.36139413e-02  1.55187808e+00
  3.83658646e-01  1.45603194e+00 -3.17762926e-02  9.60053082e-01
  4.10763374e-01  1.62567903e+00  1.33149595e-02  7.46648567e-02
  1.97523365e-02 -1.16860384e+00 -2.87958594e-03 -1.57362237e+00
 -1.28746538e+00 -1.25899561e+00  8.37318134e-02  7.42149496e-02
 -1.08713055e+00 -1.06894127e+00  4.44763270e-02 -1.03432565e+00
 -1.09227633e+00 -1.59380858e+00  8.65721377e-02  6.76256426e-02
 -1.13848875e+00 -1.07253778e+00 -3.87649917e-02 -1.16112620e+00
 -1.20768521e+00  2.25370733e-01  2.38637148e-02 -1.23131596e+00
 -8.44707691e-01  1.08240810e-01 -1.09456305e+00  1.44985373e+00
 -1.11918178e+00  1.11526433e-01 -1.00346216e+00 -1.39199724e-02
  2.52400989e-01 -1.26478013e+00 -1.38160548e+00  1.81728412e+00
  1.88392388e+00  1.88381131e+00  1.59729928e+00  1.94781842e-01
  1.73517223e+00 -9.49785706e-01  1.16873184e-01  2.22775930e-02
  1.46310430e-01 -1.19800318e+00  3.95371753e-02  1.92519571e-01
  1.36067526e-01  1.53213157e+00  1.49169903e-01 -1.05339681e+00
 -9.51683895e-01  1.62296546e+00 -1.27418211e+00  1.84554033e+00
 -4.61666836e-03 -1.08242299e+00  1.30377768e-01 -2.76375453e-02
  1.88864631e+00 -3.05505081e-02 -3.04672992e-02  1.62418274e+00
 -9.92039197e-01 -1.63836166e+00 -5.39609903e-02 -5.38726024e-02
  3.63765514e-01  1.76806048e+00 -9.70983331e-01  1.74342499e-01
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  4.76677531e-01  8.97977344e-01  1.85156448e+00 -1.15136581e+00
  3.64919032e-01  1.23049262e-01  2.55844877e-01  4.42297840e-01
  1.72802148e+00  3.26785471e-01  2.17110369e-01  1.49287944e-01
  1.04304163e+00  1.62667447e+00  4.83899311e-01 -1.20043843e+00
 -1.26914311e+00 -1.16643399e+00  1.81136761e-01  1.19741262e-01
  3.96712474e-01  3.96925582e-01 -9.92687446e-01  1.53226580e+00
  1.08063662e-01  3.60935083e-01  1.01729135e-01  1.63014742e+00
  1.61457837e+00  1.07572009e+00 -9.14439432e-01  7.36025553e-02
 -1.06388358e+00  5.97396140e-01  1.81657736e+00 -9.47287513e-01
  2.56188083e-01  7.87773107e-02  1.81690787e+00 -1.23518824e+00
  1.07589613e-01  1.10526758e-01  1.23040484e-01  1.05228621e+00
  1.32897301e-01  1.74453755e-01 -1.00361753e+00  1.60835808e+00
  5.78074154e-02 -1.02195112e+00  5.31839293e-01  5.31617526e-01
  4.96688930e-01  1.31593102e-01 -1.08978245e+00  1.81247111e+00
  1.88227218e+00 -2.30785306e-02  2.95819776e-01  1.89010335e+00
  2.32454885e-01 -9.74354978e-01  2.61436872e-01 -1.13515428e+00
 -1.48767936e+00  1.94704938e-02 -1.27222723e+00 -1.00174779e+00
  2.71576910e-02 -1.06647307e+00  1.23101689e-01 -1.03888672e+00
 -9.26190481e-01 -1.10752599e+00 -8.71415243e-01 -1.21905108e+00
  1.61718096e+00 -1.18820163e+00 -1.01555911e+00  1.01525467e+00
 -1.59736305e+00  1.83565622e-01  1.71898154e-01 -1.20176922e+00
 -1.02018732e+00  3.54988208e-01  3.20121200e-02 -9.92008654e-01
 -1.55002630e+00  1.62639966e+00 -3.15208627e-02 -1.02794471e+00
 -9.29802486e-01 -3.53809942e-02  1.73991277e+00 -1.11885378e+00
 -1.03742829e+00  1.82671889e+00  1.77719492e+00  1.73046429e+00
 -1.05707094e+00 -9.56541949e-01  3.25727775e-02  4.52383143e-01
 -1.26440033e+00  4.25617467e-01  3.20342966e-02 -1.05456274e+00
  1.37627860e-01 -1.00744599e+00 -1.00014629e+00  1.51139979e+00
  1.81048887e+00  9.75963248e-01  2.02047271e-01 -1.00780055e+00
  1.16923022e-01  1.59742271e+00  8.10912649e-02  1.87827940e+00
  1.87699925e+00 -2.09184452e-02 -4.13692077e-02  7.39050076e-02
 -2.37719294e-02 -1.62077911e+00  1.81734535e+00 -1.05569150e+00
  1.72920027e+00  1.23676011e-01  1.30409758e-01 -1.60690428e+00
  2.25706700e-02 -9.51904772e-01  4.12873595e-01 -1.06882352e+00
 -1.07472490e+00  5.73738262e-01 -1.19034095e+00 -1.11490315e+00
 -1.89609773e-02 -1.25963036e+00  1.73041564e+00  1.35776123e-01
 -4.84722560e-02  1.50182753e+00 -9.57393267e-01  1.68979893e+00
  4.70339059e-01 -1.15491013e+00]
Unique values in p90_1200: [-0.46440209 -0.7740401  -1.00290733 -0.8891155   0.05141904 -1.24058306
  0.69883952 -0.76724366  0.50880788 -0.57167349  0.18291459  0.57258939
 -1.27805142  0.06479819  0.85372265  0.84543013 -0.6490723  -1.6064613
  0.37333094 -0.86672327  0.86078777 -1.31138878  0.50851477  0.29350177
 -0.65205834  0.06984209  0.64829062 -0.67736943 -0.62796853  0.6350397
 -0.71110738  0.79154102 -1.05950158 -0.77720322 -0.78395081 -0.64705108
  0.90857047 -0.5701591   0.74174931  2.67399014  0.11269081  1.16547958
 -0.27110351  0.9816032   0.1486576   1.55046465  0.3781489   0.62774864
  0.47835518 -0.67160498 -0.48251372 -1.42507741 -1.15260915 -0.9839237
  0.80234327  0.75669781 -1.15014888 -0.61459548  0.52570433 -0.65929444
 -0.53586547 -1.34364287  0.59903627  0.54558682 -0.56628763 -0.62793189
 -0.63342767 -0.67242934 -0.8845479   0.91855446  0.3178908  -1.07634125
 -1.27633429  0.6296172  -0.5388454   1.82399542 -0.90241527  0.81421414
 -0.72904803  0.22968973 -0.7390137  -1.05742418 -1.35069394 -0.37392333
 -0.711889   -0.71471627  2.08183271  0.18324434  0.98315423 -0.97212611
  0.8582475   0.45727584  0.91785222 -0.94753557  0.40770395  0.74049749
  0.76753059  2.15249615  0.94064136 -0.60117969 -0.84536914  2.92471955
 -1.0439864  -0.51709435 -0.12620437 -0.8587605   0.87094884 -0.1821941
 -0.79025874  0.06911543  0.0693719   2.35121723 -0.6836224  -1.32415607
 -0.4859272  -0.48562799 -0.01430431  0.16600592 -0.76295085  1.02837223
 -0.53078493 -0.69213475  0.90401508  2.41533459 -0.61427184  0.60393362
 -0.39919168 -0.69796637  0.36771915  0.84754295 -1.53399388 -1.44546368
  1.46750289  0.24056526  0.85630566  0.73031199 -0.24171944  1.51197591
 -0.22698465 -0.97850121 -0.99092165 -0.69648862  0.31593674  0.84623007
 -0.71653598 -0.71450865 -0.68703589  2.15483491  0.62829211 -0.73497125
  0.88767432  1.40020408  0.86992297  0.56721574 -0.9887783   0.73751756
 -0.5535985  -0.54273519 -0.37507134 -0.85516383  0.87461269  0.61212843
 -0.37357526 -1.08455193  0.88601948  0.79191351  0.847488    3.39251372
  0.8855676   1.02840887 -0.72869996  2.4291473   0.60823864 -0.6007156
 -0.42425241 -0.4234891  -0.4473713   0.88510962 -0.57680898 -0.37157236
 -0.74491249  0.06275255 -0.84673697 -0.73718788 -0.03588438 -0.73810384
  0.8074238  -0.56798521 -1.28354291  0.36280959 -1.13872683 -0.66234154
  0.39103955 -0.56876683  0.8495825  -0.64845556 -0.93073072 -0.5016329
 -0.69034557 -1.13338677 -1.03037337  3.32806661 -0.80016945 -0.69179889
  3.16020736 -1.31297401  0.78458581  0.26632822 -1.0344262  -0.77791156
  0.39768333  0.46518364 -0.68366515 -1.32773504  2.80546126 -0.05791022
 -0.61694035 -0.93657456 -0.25327277  0.60168645 -0.52231533 -0.57502591
 -0.31515519  0.11951778  1.41507931 -0.57164906 -0.83406005  0.30977537
 -0.23249264 -0.92990635  0.19970723  0.78081205  0.46526913 -1.21544844
  0.6839643  -0.63543668 -0.68036158  1.90550386 -0.36249212  1.86577552
  0.99139789 -0.64626335  0.72317359  3.44041245  0.63914931 -0.69222634
 -0.68616267 -0.30841981 -0.17197196  0.74752598 -0.43544547 -1.43403064
 -0.37447901 -1.23962313  1.50649846  0.64397338  0.87075955 -1.32239681
  0.01604457 -0.87118706 -0.7420669  -0.61297728 -0.65685188 -0.45919332
 -1.01355569 -0.51319845  0.15930108 -0.94522124  1.41655707  0.7829554
 -0.2453955   1.79283439 -0.85491957  2.35702443  0.06105496 -0.61618926]
Unique values in p00_1200: [-5.08015517e-01 -7.31805479e-01 -9.16579967e-01 -8.13687716e-01
  2.97992988e-02 -1.16297618e+00  5.78638089e-01 -7.94808204e-01
  3.91083025e-01 -5.33384862e-01  8.97920089e-02  4.69818677e-01
 -1.23635936e+00  6.84452123e-03  7.15667514e-01  7.07943084e-01
 -6.21822486e-01 -1.53698152e+00  3.13052077e-01 -8.08000919e-01
  6.93772524e-01 -1.22738722e+00  4.10202902e-01  2.15109143e-01
 -6.05215507e-01  1.77428509e-02  5.26550083e-01 -6.04756630e-01
 -5.94857997e-01  5.14919735e-01 -6.55500783e-01  6.84278139e-01
 -9.27685884e-01 -7.29762384e-01 -7.15717468e-01 -6.03494718e-01
  7.81478129e-01 -5.18684408e-01  6.16451743e-01  2.71049887e+00
  3.03728951e-02  1.29619613e+00 -2.99816449e-01  1.44487229e+00
  1.10945967e-02  1.70117151e+00  2.93577117e-01  5.15482405e-01
  3.94213222e-01 -6.18304428e-01 -4.57473489e-01 -1.33566964e+00
 -1.07854608e+00 -9.19213047e-01  6.94264178e-01  6.57062361e-01
 -1.06389698e+00 -5.59966953e-01  4.26514888e-01 -6.27219754e-01
 -5.00252847e-01 -1.24745271e+00  4.91019889e-01  4.42919742e-01
 -5.29183952e-01 -5.94819757e-01 -6.34037355e-01 -6.25848585e-01
 -8.26552662e-01  7.41047784e-01  2.37097001e-01 -1.02156557e+00
 -1.19926898e+00  5.15580736e-01 -5.03530540e-01  1.87360548e+00
 -8.21532329e-01  6.80885727e-01 -6.96395467e-01  1.56219923e-01
 -7.63282259e-01 -9.95605695e-01 -1.26138673e+00 -3.73608247e-01
 -7.16569669e-01 -7.19355708e-01  2.22731228e+00  1.18559229e-01
  9.67339719e-01 -9.38529585e-01  7.22015313e-01  3.95770126e-01
  7.71098768e-01 -8.62492567e-01  3.18859057e-01  5.95184977e-01
  6.34823213e-01  2.29751500e+00  7.88295731e-01 -5.48025225e-01
 -8.10317155e-01  2.94563512e+00 -9.78550218e-01 -5.15805501e-01
 -1.68910848e-01 -7.93278614e-01  7.30728514e-01 -2.17267747e-01
 -7.80506536e-01  1.30612125e-02  1.32906511e-02  2.36799087e+00
 -6.41657992e-01 -1.23347007e+00 -4.96958766e-01 -4.96685624e-01
 -1.45229515e-01  1.62239953e-01 -7.25807301e-01  8.59066589e-01
 -5.06458613e-01 -6.98154496e-01  1.12211785e+00  2.91903117e+00
 -6.77073466e-01  1.02303319e+00 -3.99911735e-01 -6.63869826e-01
  2.14371662e-01  7.10253857e-01 -1.42517087e+00 -1.39896189e+00
  1.43698944e+00  1.42338893e-01  7.03228668e-01  5.98839605e-01
 -1.07847425e-01  1.66393145e+00 -3.41224639e-01 -9.31274958e-01
 -8.70566618e-01 -6.46339630e-01  2.39833875e-01  7.08680565e-01
 -7.63260407e-01 -7.61539619e-01 -6.47443120e-01  2.29979300e+00
  5.14428081e-01 -7.76742652e-01  7.63784049e-01  1.55343495e+00
  1.05531847e+00  7.66444443e-01 -9.52416079e-01  6.41657203e-01
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  7.14503933e-01  5.02016549e-01 -3.73258626e-01 -1.02905510e+00
  7.57212274e-01  6.61061146e-01  7.10204692e-01  3.96361012e+00
  7.43358558e-01  8.59061126e-01 -6.96045847e-01  2.53884062e+00
  4.99978917e-01 -5.56541764e-01 -5.05715669e-01 -5.05081982e-01
 -5.28741463e-01  7.43216525e-01 -5.22847079e-01 -3.71128126e-01
 -7.36656465e-01  5.41326189e-03 -8.66633386e-01 -7.38371791e-01
 -1.13173676e-01 -6.95259200e-01  6.64409856e-01 -5.31735090e-01
 -1.18653187e+00  2.78931292e-01 -1.07826201e+00 -6.21664064e-01
  3.05027192e-01 -5.33057093e-01  7.12198622e-01 -6.15474687e-01
 -8.93488619e-01 -4.64820984e-01 -6.55915957e-01 -1.09147931e+00
 -9.78867062e-01  3.41427422e+00 -7.47980895e-01 -6.58423392e-01
  3.68922351e+00 -1.21405629e+00  6.39160693e-01  1.74148905e-01
 -9.43511679e-01 -7.48226722e-01  2.39265742e-01  3.71793801e-01
 -6.41690769e-01 -1.23407207e+00  2.89648064e+00 -7.50213277e-02
 -5.77835844e-01 -9.00715932e-01 -2.82586709e-01  6.00008649e-01
 -4.61046174e-01 -5.24398520e-01 -3.16849528e-01  1.08436620e-01
  1.38337731e+00 -5.33363011e-01 -7.98593940e-01  2.30361342e-01
 -3.38963031e-01 -8.43339914e-01  8.48372295e-02  6.76531858e-01
  3.71875743e-01 -1.12569679e+00  5.58862674e-01 -5.87887436e-01
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  8.14511813e-01 -6.06908982e-01  5.97725189e-01  3.45685145e+00
  5.25555849e-01 -6.98247364e-01 -6.78133254e-01 -3.36024032e-01
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 -3.74159992e-01 -1.14549351e+00  1.47197882e+00  5.25462981e-01
  7.30559166e-01 -1.22421987e+00 -2.22376312e-03 -8.36391204e-01
 -7.85614275e-01 -5.58470140e-01 -5.99588801e-01 -5.06158158e-01
 -9.19344155e-01 -4.54496251e-01  9.20863941e-02 -8.73795146e-01
  1.38488505e+00  6.49332468e-01 -2.74556360e-01  1.89964129e+00
 -8.20254028e-01  2.34002122e+00 -3.73934105e-02 -5.77300487e-01]
Unique values in p10_1200: [-0.55484798 -0.67951526 -0.85797515 -0.76463416  0.05124564 -1.11664732
  0.51146131 -0.84850242  0.30479419 -0.48019628  0.01334469  0.42912495
 -1.23111107 -0.0179615   0.63616877  0.62671111 -0.56991594 -1.49883829
  0.30649687 -0.78248467  0.56645936 -1.17270967  0.3569444   0.17895664
 -0.55330602  0.00496691  0.4466741  -0.55270331 -0.54257261  0.43652834
 -0.60408003  0.6406992  -0.82873326 -0.67968603 -0.66533631 -0.55133212
  0.70820366 -0.46522375  0.54923166  2.66640055 -0.04251726  1.32803925
 -0.31753264  1.7730162  -0.11300517  1.70267394  0.26779231  0.45630253
  0.37236395 -0.56660099 -0.41019557 -1.29437692 -1.03291868 -0.8717774
  0.64900165  0.62412947 -1.02310994 -0.50709761  0.38718078 -0.57537556
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 -0.63164435 -0.5742756  -0.77777844  0.61325542  0.20095586 -0.97548514
 -1.15459396  0.44358517 -0.44988457  1.88571449 -0.77552327  0.60657027
 -0.6455571   0.13171356 -0.7902296  -0.94929201 -1.21886484 -0.40629799
 -0.73780315 -0.74051538  2.21440194  0.07830769  0.91335895 -0.89127032
  0.64559126  0.3902245   0.67687737 -0.81408218  0.27513543  0.49449479
  0.54882985  2.31092227  0.69287452 -0.49495284 -0.7611836   2.88741739
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  1.37373535  0.06308905  0.59178358  0.51151153  0.02335986  1.66704826
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  0.70215638  1.56022144  1.08171829  0.959487   -0.90531366  0.6125623
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  4.16956013 -1.17081362  0.53892518  0.10300408 -0.89642356 -0.69812419
  0.10125619  0.33710995 -0.59000655 -1.19096852  2.84675399 -0.04930286
 -0.52528464 -0.85290729 -0.30087752  0.55519355 -0.40665459 -0.47093953
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  0.47644337 -0.53548565 -0.59035312  2.05107028 -0.39501208  2.62165876
  0.69035817 -0.55474753  0.51987928  3.39411772  0.46414791 -0.72030925
 -0.6942467  -0.35322361 -0.22541717  0.61943329 -0.37497672 -1.3059702
 -0.40684043 -1.10289178  1.40765835  0.44915027  0.6451995  -1.18111257
  0.02109465 -0.787658   -0.82852231 -0.50558077 -0.54921758 -0.56120164
 -0.8734399  -0.40004478  0.06768478 -0.8256594   1.32217782  0.56272251
 -0.28938569  1.91377103 -0.77126406  2.27052005 -0.13138306 -0.52473717]
Unique values in p90u_1200: [-5.93106582e-01 -9.64721121e-01 -1.18332857e+00 -9.08713196e-01
  1.76309166e-01 -1.47426642e+00  9.96001468e-01 -9.08642492e-01
  7.05455799e-01 -3.89150151e-01 -2.90171376e-02  9.20271789e-01
 -1.55821312e+00  3.29105256e-01  1.14480946e+00  1.14158363e+00
 -4.96839854e-01 -1.93156417e+00  6.77749011e-01 -1.15469932e+00
  9.12936339e-01 -1.60116086e+00  8.24358576e-01  5.91413862e-01
 -5.59964491e-01  3.45543733e-01  8.84666135e-01 -5.76668105e-01
 -4.68945260e-01  8.69343441e-01 -5.77341994e-01  1.12399623e+00
 -1.32912173e+00 -7.58739708e-01 -7.56364525e-01 -4.84069101e-01
  1.13434760e+00 -3.67298465e-01  1.05935810e+00  2.41397480e+00
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 -4.03721624e-01 -4.68912118e-01 -5.28700451e-01 -5.94045608e-01
 -9.04216918e-01  1.02038300e+00  6.17319930e-01 -1.15933259e+00
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  1.23967317e+00 -9.47910347e-01  1.15801106e+00  7.59245414e-01
  1.18264668e+00 -9.80113413e-01  7.15641469e-01  9.59081178e-01
  1.01253937e+00  1.54743063e+00  1.19273292e+00 -4.04947881e-01
 -7.76459679e-01  2.83802787e+00 -1.12989468e+00 -4.07521917e-01
  5.74947685e-02 -8.64130454e-01  1.16458424e+00  3.04231221e-03
 -8.45833810e-01  3.54679903e-01  3.54867708e-01  2.21394019e+00
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  1.77358122e+00  5.21147023e-02  9.49160645e-01  9.65223512e-01
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 -1.26388263e+00 -6.36887285e-01  3.17063629e-01  1.13611518e+00
 -9.00829797e-01 -8.99125188e-01 -5.39957715e-01  1.55039133e+00
  8.82036863e-01 -8.97416161e-01  1.23333198e+00  1.29962722e+00
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  9.27308959e-01  9.23851135e-01 -2.94706238e-01 -1.17012144e+00
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  1.16634077e+00  1.26190047e+00 -6.03237015e-01  2.28399152e+00
  9.55711732e-01 -4.17099982e-01 -6.01381058e-01 -6.01149063e-01
 -6.06694840e-01  1.17400764e+00 -3.87283147e-01 -2.93402649e-01
 -8.10364039e-01  3.27558625e-01 -1.00566265e+00 -7.14495016e-01
 -9.21970113e-02 -6.11146927e-01  8.27462885e-01 -4.04472845e-01
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  7.17486378e-01 -3.84587590e-01  1.14588105e+00 -4.89404978e-01
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  7.99877618e-01 -1.43364526e+00  9.26933349e-01 -4.61963326e-01
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  1.14959297e+00 -4.82411996e-01  9.85362856e-01  3.32128375e+00
  9.46155762e-01 -6.47779986e-01 -7.66815331e-01 -1.59585931e-01
  1.91383213e-02  1.08791553e+00 -5.14327831e-01 -1.64718970e+00
 -2.95645264e-01 -1.48979569e+00  1.76662138e+00  8.91990537e-01
  1.16426387e+00 -1.44591667e+00  1.22972494e-01 -8.13567775e-01
 -9.10895050e-01 -4.33969306e-01 -5.20923107e-01 -6.59070391e-01
 -1.10477408e+00 -2.94330627e-01  4.60933448e-01 -9.83681711e-01
  1.70146403e+00  1.02723237e+00 -6.81137582e-02  8.39405086e-01
 -7.89760706e-01  2.53800356e+00 -1.41744438e-01 -4.79517587e-01]
Unique values in p00u_1200: [-6.60152234e-01 -9.53321916e-01 -1.12000468e+00 -8.41270259e-01
  1.48003467e-01 -1.43765186e+00  8.91488928e-01 -9.72020972e-01
  5.95329216e-01 -3.59929308e-01 -1.04319025e-01  8.26456825e-01
 -1.55891554e+00  2.60987699e-01  1.02811838e+00  1.02586448e+00
 -4.89610972e-01 -1.92443294e+00  6.21962395e-01 -1.12816005e+00
  7.77201986e-01 -1.56488526e+00  7.31028159e-01  5.09950026e-01
 -5.22933463e-01  2.86049355e-01  7.77532833e-01 -4.97158418e-01
 -4.51325777e-01  7.63296075e-01 -5.29478029e-01  1.03764057e+00
 -1.22392990e+00 -7.28927043e-01 -6.92854387e-01 -4.51646285e-01
  1.03529362e+00 -3.14861750e-01  9.50214261e-01  2.59268325e+00
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 -1.47298111e-01  1.67664043e+00  6.21983073e-01  8.43753917e-01
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 -7.27055690e-01 -7.29268229e-01  2.12088516e+00  3.41197442e-02
  1.27724613e+00 -9.52719154e-01  1.04350276e+00  7.05263453e-01
  1.06281595e+00 -9.01419268e-01  6.28114081e-01  8.31584953e-01
  9.01331624e-01  1.82283342e+00  1.06793374e+00 -3.52092371e-01
 -7.71844095e-01  3.00451532e+00 -1.08861867e+00 -4.25840223e-01
 -9.19015166e-04 -8.10666916e-01  1.04791750e+00 -4.88297886e-02
 -8.74024107e-01  2.89140706e-01  2.89316469e-01  2.35133040e+00
 -5.07125182e-01 -1.38099742e+00 -4.05151950e-01 -4.04914154e-01
 -3.07283287e-01  3.70001768e-01 -6.33229563e-01  1.12426043e+00
 -6.46577170e-01 -6.92058286e-01 -2.32997809e-01  7.29074094e-01
 -8.69898859e-01 -1.57998942e-01 -3.35581040e-01 -5.79497952e-01
  6.97891806e-02  1.02642279e+00 -1.80649968e+00 -1.74589473e+00
  1.81907004e+00 -2.67250770e-02  8.29062245e-01  8.53648308e-01
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  7.10557004e-01 -1.37097690e+00  8.18464803e-01 -4.21932093e-01
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  1.00494875e+00 -4.56464243e-01  8.78523863e-01  3.52063656e+00
  8.44250187e-01 -6.92337438e-01 -7.91653556e-01 -2.08463440e-01
 -3.23804920e-02  1.00865010e+00 -5.18074148e-01 -1.59699396e+00
 -3.09868029e-01 -1.42797562e+00  1.81046802e+00  7.88088918e-01
  1.04761767e+00 -1.36717940e+00  9.70426979e-02 -8.10935729e-01
 -9.94730513e-01 -3.79645718e-01 -4.65293721e-01 -7.25949420e-01
 -1.01781226e+00 -2.25491719e-01  3.85768692e-01 -9.25235078e-01
  1.74232388e+00  9.15940585e-01 -1.16074430e-01  1.05189800e+00
 -7.86266954e-01  2.63471115e+00 -2.34248824e-01 -4.52401029e-01]
Unique values in p10u_1200: [-7.34917416e-01 -9.19337092e-01 -1.07985274e+00 -8.01030316e-01
  1.79466807e-01 -1.42813120e+00  8.43131026e-01 -1.07547678e+00
  5.16117799e-01 -2.94006913e-01 -1.76696467e-01  8.04402400e-01
 -1.60449737e+00  2.34136711e-01  9.74370361e-01  9.69694516e-01
 -4.30533702e-01 -1.94218850e+00  6.31583555e-01 -1.13707266e+00
  6.68871941e-01 -1.55169978e+00  6.89321366e-01  4.80072359e-01
 -4.65987263e-01  2.75716221e-01  7.11428052e-01 -4.38741093e-01
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  9.88427491e-01 -1.35509430e+00  1.29541802e-01 -7.69167489e-01
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 -9.87612324e-01 -1.52241994e-01  3.64764438e-01 -8.89496519e-01
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 -7.43154401e-01  2.64459411e+00 -3.40844283e-01 -3.91292115e-01]
Unique values in p90r_1200: [-2.52291243e-01 -4.44835359e-01 -6.44419989e-01 -7.14628835e-01
 -8.50404057e-02 -7.86412518e-01  2.73973898e-01 -4.89553507e-01
  2.19599157e-01 -6.58732279e-01  3.67550345e-01  1.18071814e-01
 -7.69910574e-01 -2.16348637e-01  4.08150904e-01  3.96114006e-01
 -6.91751961e-01 -9.95369865e-01 -2.38741515e-03 -4.22071295e-01
  6.57981405e-01 -7.87642155e-01  9.76406742e-02 -6.17089038e-02
 -6.32805003e-01 -2.23825727e-01  2.94279818e-01 -6.62514820e-01
 -6.81252672e-01  2.85446743e-01 -7.24148928e-01  3.14529332e-01
 -6.00096679e-01 -6.61024591e-01 -6.75920112e-01 -7.01056584e-01
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  8.99982845e-01  3.91195461e-01  6.12710485e-01  3.63545567e-01
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  4.19759122e-01 -9.66507188e-01 -9.59379161e-02 -7.78661298e-01
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  3.97467734e-01 -3.83792380e-01  2.45494250e+00 -7.72924875e-01
  1.76270241e+00  2.57537384e-01 -6.48693257e-01]
Unique values in p00r_1200: [-2.82517974e-01 -4.04765227e-01 -5.76372446e-01 -6.55143259e-01
 -8.58730487e-02 -7.15787498e-01  1.90970674e-01 -4.99038342e-01
  1.35828972e-01 -6.09035762e-01  2.57183371e-01  5.88264161e-02
 -7.32310620e-01 -2.32547261e-01  3.06033413e-01  2.94429225e-01
 -6.44386362e-01 -9.23068975e-01 -2.76175081e-02 -3.76040744e-01
  5.02461184e-01 -7.10791009e-01  4.25185099e-02 -9.64011584e-02
 -5.83674263e-01 -2.36708094e-01  2.05386235e-01 -6.07021267e-01
 -6.32432207e-01  1.98096052e-01 -6.66768774e-01  2.41391207e-01
 -4.98634086e-01 -6.11536915e-01 -6.20444122e-01 -6.47455607e-01
  4.16084151e-01 -6.25207946e-01  2.03011109e-01  2.37905312e+00
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  8.23907835e-01 -6.09014434e-01 -7.06958815e-01 -7.44957102e-02
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 -6.30244839e-03 -7.12149193e-01  2.24348466e-01 -6.47622350e-01
 -6.60127146e-01  2.35971281e+00 -3.63598961e-01  3.63583410e+00
  5.03168875e-01 -6.48998954e-01  2.36989953e-01  2.83344866e+00
  1.41063944e-01 -5.89923268e-01 -4.61099339e-01 -4.00821548e-01
 -3.37432827e-01  2.07887388e-01 -2.58650381e-01 -8.93100643e-01
 -3.73412594e-01 -6.93835517e-01  9.14618261e-01  1.93559076e-01
  3.14186396e-01 -8.90550242e-01 -9.49237331e-02 -7.23867774e-01
 -4.61419254e-01 -6.35071021e-01 -6.27737213e-01 -2.17518045e-01
 -6.77098149e-01 -5.95093287e-01 -1.98275646e-01 -6.83076681e-01
  8.23956307e-01  2.93498564e-01 -3.78368367e-01  2.38483098e+00
 -7.18363298e-01  1.68219114e+00  1.53307961e-01 -6.00271062e-01]
Unique values in p10r_1200: [-0.30872637 -0.361318   -0.52663565 -0.60973226 -0.06855705 -0.66565705
  0.14046349 -0.51425322  0.07182578 -0.56481387  0.17659357  0.03344669
 -0.70781225 -0.23440862  0.23941132  0.22730788 -0.59934647 -0.87152212
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Unique values in p00r_30: [-1.65937436e-01 -5.12415908e-01 -4.63430960e-01 -5.57547809e-01
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Unique values in p10r_30: [-1.65099223e-01 -4.79141390e-01 -4.31183812e-01 -5.28877467e-01
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Unique values in p90u_75: [-2.62215368e-01 -8.23518126e-01 -5.12601028e-01 -8.19942943e-01
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Unique values in p00u_75: [-2.31281453e-01 -7.74645478e-01 -4.84143837e-01 -7.67564219e-01
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Unique values in p10u_75: [-0.23503958 -0.75847082 -0.45888469 -0.75199427 -0.59165514 -0.30382469
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 -2.63562464e-01 -2.40529527e-01 -5.70601424e-01  2.93956540e-01]
Unique values in p90u_150: [-0.79090501 -1.00391685  0.4674096  -0.91817178 -0.33014982  0.63867743
  2.24962462 -0.90032892 -0.69878474 -0.15542671 -0.34985475 -0.41951651
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 -0.14311976 -0.27348088 -0.22746101 -0.21942029 -0.42563907  0.48638983]
Unique values in p00u_150: [-0.7736289  -0.99117993  0.47079923 -0.89372018 -0.35619204  0.65221175
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  2.3572528  -0.40671796 -0.68486367  0.04210939  0.40413883  0.30283976
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 -0.7168091  -0.82192974  0.0522485   0.35258848  0.40897826 -0.15644626
 -0.06853055 -0.25711936 -0.29959185 -0.49637875  0.55521293]
Unique values in p10u_150: [-0.77952739 -0.99148735  0.56790966 -0.89066426 -0.31168517  0.72525357
  2.00140279 -0.89359516 -0.7078971  -0.17222914 -0.51533361 -0.44371707
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 -0.82699928 -1.04924615 -0.23015855 -0.60692028  1.45862942  1.24722939
  0.40206675 -0.90870713 -0.24439437 -0.80090486 -0.90996573 -0.25138729
 -0.9314057  -0.31430611 -1.01023626  0.44208702 -0.6914278   0.12624309
  0.61535405 -0.49359242 -0.31606965  2.09965368 -0.77504542 -0.84483342
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  2.19059294 -0.37826855 -0.69283263 -0.00605003  0.32130129  0.36373378
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 -0.13680708 -0.0502848  -0.22397303 -0.31039532 -0.56752119  0.6327145 ]
Unique values in p90r_150: [-6.28048866e-01 -3.90583101e-01 -1.27851351e-01 -6.61753364e-01
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 -7.37976000e-01 -8.42355189e-01 -3.54579026e-01 -8.05488138e-01
  6.64979758e-01 -8.09386592e-01  2.87129665e-01  4.83994088e-02
 -6.30323116e-01 -4.86067496e-01  1.74165968e-02 -5.07570103e-01
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Unique values in p00_600: [-1.06620192 -0.76785878 -0.39838346 -0.96784484 -0.65392596 -1.04695935
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Unique values in p10_600: [-1.06882346e+00 -7.21464537e-01 -3.27728687e-01 -9.34511551e-01
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Unique values in p90u_600: [-1.16208916 -0.90181161 -0.41087373 -1.05698568 -0.71049175 -1.06853125
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  1.121597    0.30303986  1.54896214  3.7720067   1.83506557  0.27626428
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 -0.24177984  0.37642534 -0.04184555  0.04384129 -0.65146613 -0.49218747]
Unique values in p00u_600: [-1.19885574 -0.93195859 -0.32698204 -1.03834114 -0.7138696  -1.06051803
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  0.23393768 -0.69129834  1.79614818  2.10534178 -0.60311343 -0.68635695
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  0.28348943  0.55176923 -1.32596128 -1.25061687 -1.47777831  0.55514506
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  0.88332028  0.28918629  0.36983796 -0.11803938  1.41746619 -0.59593653
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 -0.28571188  0.3138125   0.801811    0.68544112  0.05555329  1.0334627
  0.31082391  1.47730935  4.03528954  1.78339082  0.23614231 -1.42006565
 -0.4102992  -0.03707651  0.06812534 -0.74281953 -1.34359589  0.55460103
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  0.71752219 -0.05129253  0.07772543 -0.75549848 -0.48817133]
Unique values in p10u_600: [-1.23376266e+00 -9.07064174e-01 -2.45380816e-01 -1.02670392e+00
 -6.72779408e-01 -1.05793120e+00  1.66066940e+00 -1.23357725e+00
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 -1.33236599e+00  6.05669184e-01  1.48777708e+00  1.62269397e+00
 -6.62119279e-01 -1.66864645e+00 -4.16611277e-01 -1.47918254e+00
 -3.13518967e-02 -1.38377130e+00  1.66364517e+00  1.29819756e+00
 -3.91745274e-01 -7.72427226e-03  4.87743944e-01 -8.17056247e-01
 -4.53977809e-01  4.74575011e-01 -1.02193124e+00  7.45940177e-01
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  4.83827517e-02  6.22544243e-02  8.35922925e-01 -5.84035039e-01
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  8.78682470e-01  1.21640963e+00  1.67856980e+00 -8.89486522e-01
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  2.20148851e-01 -5.77174742e-01  2.99718688e-01  6.10636433e-01
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 -7.15973873e-01 -4.07718299e-01 -4.11605114e-01  1.49233230e+00
 -1.34058828e+00 -1.33937966e+00  2.59454212e-01  1.22242984e+00
  1.46836590e+00 -1.32438864e+00  1.11887300e+00  1.03686317e-01
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  1.25907761e+00  1.73584196e+00  1.64441711e+00  9.70130212e-01
  1.70873955e+00  1.18223404e+00  3.81380720e-01  1.85546796e+00
  1.64938435e+00  7.10031920e-02 -5.74118854e-01 -5.73301661e-01
 -7.54111817e-01  1.67683012e+00 -7.89958416e-01  5.34117907e-01
 -1.33452686e+00  5.92923726e-01 -1.32965575e+00 -2.13095986e-01
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 -9.19054243e-01  1.30181426e+00 -1.37238783e+00  2.98322364e-01
  1.42404979e+00  2.46418045e-01  1.64256297e+00  2.93272709e-01
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  2.00318769e-01 -8.47457185e-01  1.65290950e+00 -7.94003176e-01
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 -7.08582515e-01 -1.02135439e+00 -7.95241554e-01  4.33486476e-01
  1.17678609e+00 -1.22410972e+00  5.56655941e-01 -2.25422547e-01
  4.12360789e-01  8.53548647e-01  5.84419888e-01  2.35966220e-01
  9.34810095e-01  4.08929496e-01  1.41818981e+00  4.17499016e+00
  1.74215975e+00  1.45340249e-01 -1.45457222e+00 -4.13887302e-01
 -3.42933322e-02  8.06218006e-02 -7.03622133e-01 -1.35116142e+00
  4.53227286e-01 -1.22784317e+00  2.83971598e-03  6.18261275e-01
  1.57533343e+00 -8.96754271e-01 -6.60752714e-01 -1.48592563e-02
 -1.33297717e+00 -8.84578788e-01 -8.81969266e-01 -1.02425463e+00
 -1.23444938e+00 -7.09816316e-01  6.26337060e-01 -1.22405249e+00
  2.41393570e-01  1.08893216e+00 -2.84178007e-01  7.68739164e-01
  2.40478094e-02  5.96014097e-02 -8.70089071e-01 -4.40802009e-01]
Unique values in p90r_600: [-6.52492040e-01 -3.68825967e-01 -4.49829449e-01 -6.88846984e-01
 -4.31625383e-01 -8.25659720e-01  6.41311739e-01 -5.83994900e-01
 -8.02379880e-02 -4.32111156e-01  4.30305559e-01  3.97631140e-01
 -6.93379917e-01 -2.35632061e-01  7.75718304e-01  8.81896867e-01
 -7.33982713e-01 -9.62729706e-01 -3.37034449e-01 -6.68895910e-01
  1.12591432e+00 -7.16393487e-01  4.66547038e-01  5.38742768e-02
 -5.84824614e-01 -3.57291525e-01  3.05174770e-01 -5.85622416e-01
 -6.47188963e-01  2.88576945e-01 -7.41474959e-01  3.46798752e-01
 -5.94915921e-01 -6.18872670e-01 -6.57669597e-01 -5.61177767e-01
  3.31211477e-01 -5.82250374e-01  7.63201680e-01  2.79089133e+00
  2.90247011e-01  1.46534177e+00 -6.02535815e-01  2.91410740e+00
  2.96235843e-01  2.65352047e-01 -4.45709264e-02  6.46694243e-01
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  1.00010274e+00 -7.35225156e-01  3.19652214e-01  3.34973556e-01
  6.59820742e-01  1.47828744e+00  1.02187386e+00 -6.47597083e-01
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  2.62483506e-01 -8.14914924e-01  3.65087916e-01 -6.06652473e-01
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  1.26916762e+00 -5.24581710e-01  6.38063799e-01  4.24094053e+00
  6.41620223e-01 -4.12648336e-01 -7.41715363e-01 -6.23667282e-01
 -4.53772363e-01 -1.11696220e-02 -3.47427148e-01 -9.20476520e-01
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  8.91747061e-01 -9.30918222e-01 -4.08070727e-01 -6.73361473e-01
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 -6.57735194e-01  1.65389247e-01 -4.74323757e-02 -6.17544064e-01]
Unique values in p00r_600: [-0.62200101 -0.36056015 -0.38658554 -0.62824953 -0.40878055 -0.75951772
  0.52573835 -0.55335697 -0.12707955 -0.40389644  0.31418025  0.324763
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 -0.33580348 -0.62020755  0.89471752 -0.63057228  0.37992956  0.01721223
 -0.5476553  -0.35391376  0.22180904 -0.52364351 -0.60603181  0.20710522
 -0.66457153  0.27522846 -0.49006637 -0.57857995 -0.60850445 -0.52036422
  0.25043242 -0.51561031  0.63662559  2.67245299  0.18504042  1.58574536
 -0.56845745  3.47534913  0.13790403  0.32610439 -0.07493188  0.54049062
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 -0.70477901  0.41400026 -0.50552718  2.24266314 -0.6493896   0.6457095
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Unique values in p10r_600: [-5.99107008e-01 -3.17109910e-01 -3.39928183e-01 -5.84963231e-01
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Unique values in p90_300: [-1.17244295e+00 -5.86884803e-01  4.14370244e-01 -1.14533496e+00
 -7.41186312e-01 -5.77644174e-01  1.89403349e+00 -1.00938375e+00
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Unique values in p00_300: [-1.13629567 -0.60026057  0.4963653  -1.09499068 -0.72815525 -0.53548603
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  0.22933833  2.2514986   0.18803582  0.57058243 -0.55169931 -0.46491276]
Unique values in p10_300: [-1.12515125 -0.55610764  0.60507609 -1.07117365 -0.6858375  -0.50278478
  1.63350571 -1.01174234 -0.7587987  -0.4556458  -0.48769443 -0.04247873
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  0.21010872 -0.67667964  1.77291165  2.43500767 -0.73166546 -0.34586754
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  1.58830813 -0.75222036  0.06246535 -0.8952964  -1.0445304  -1.09708521
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  0.20863526  0.55284382  1.58029501  3.44147467  1.75905884  0.7952099
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  0.67036449 -1.00571277  0.48917584  0.43737284  1.51298785 -0.75253748
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  0.23508012  2.27646227  0.25367815  0.54161694 -0.64098024 -0.42029833]
Unique values in p90u_300: [-1.19151445e+00 -7.16400905e-01  6.53952122e-01 -1.16935750e+00
 -7.01982421e-01 -4.13600183e-01  2.07373277e+00 -1.01856618e+00
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 -1.21179837e+00 -1.40690416e+00 -8.82244890e-01 -1.40272025e+00
 -5.74435297e-02 -1.02360260e+00  2.27707486e+00  1.93974269e+00
 -2.82513919e-01 -1.08214767e+00  4.28761797e-02 -8.43147167e-01
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  8.97645346e-02 -9.67093328e-01  7.65264825e-01 -8.58065332e-01
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  1.58352947e+00  1.41221099e+00  3.87335885e-01 -9.00013440e-01
  8.71021321e-01 -1.36399497e-01 -1.14906163e+00  2.87725838e+00
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  7.85829819e-02  1.36634085e+00 -7.68625481e-01  1.02119605e+00
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  1.16113830e+00  1.07323835e+00  1.02384845e+00  3.99950487e-01
  1.14622905e-01  9.63013005e-01  2.00348194e+00  2.55123975e+00
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  8.12668575e-01 -4.47257976e-01 -6.76577229e-01 -1.02121795e+00
  1.17739838e+00 -1.03033478e+00  7.49670990e-01  6.70306478e-01
  1.76279258e+00 -7.43968240e-01 -7.29380054e-01  5.15556051e-01
 -9.38849694e-01 -7.51196338e-01 -8.59599575e-01 -9.20808362e-01
 -1.12878776e+00 -7.65822236e-01  7.43838230e-01 -1.09529087e+00
  7.00255944e-01  1.71123424e+00  4.84167252e-01  1.19619772e+00
  6.20325755e-01  8.67853529e-01 -5.37093801e-01 -3.60175113e-01]
Unique values in p00u_300: [-1.19804562 -0.73602362  0.77950701 -1.16242274 -0.7148587  -0.38126612
  2.01089484 -1.03237611 -0.86252444 -0.42272146 -0.66838098 -0.05469897
 -0.9847137   1.08257532  0.81573316  1.56879516 -1.21772509 -1.41052369
 -0.89886227 -1.40150956 -0.12912478 -1.01115566  2.16884583  1.82858825
 -0.25189545 -1.0905873   0.010722   -0.78765962 -0.45975711 -0.04890255
 -1.06315032 -0.17987448 -1.03419303 -0.4035655  -1.0038283  -0.09080104
  0.205555   -0.7753341   2.13692711  1.74364499 -0.85380204 -0.80759553
  0.1224257   0.23098598 -0.94149994  0.18623951 -0.20648129  2.23645892
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 -0.55403019 -1.3253518  -1.11109399  2.61265903 -0.47228352 -0.54476537
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 -0.99933711 -0.17006015  0.94066641 -0.0726082   1.31178505 -0.74089237
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  1.41983719  0.30220337  0.65377017 -0.4227451   0.89717257  1.78921868
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  0.03416766  0.92235539  1.94040422  2.62578222  2.30324479  1.27477305
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  1.08219716 -0.96775695  0.8403842   0.63889801  1.70149833 -0.64443899
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 -1.13375912 -0.6992775   0.66061834 -1.09667207  0.63780491  1.62962508
  0.44685982  1.74669978  0.5693646   0.94167089 -0.64364722 -0.33008509]
Unique values in p10u_300: [-1.21575145 -0.70899805  0.93526138 -1.16641827 -0.68279415 -0.34959293
  1.93721006 -1.07953771 -0.89169235 -0.38227541 -0.74830107 -0.04821113
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  0.20478855 -0.7561083   2.07981267  1.8082403  -0.92164031 -0.80430304
  0.13059431  0.54070385 -1.00790481  0.22711438 -0.2031009   2.19530857
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  1.07006811  1.09234285  1.04922423 -0.58104465 -0.07768556 -0.96762632
  0.69255589 -0.8659705   1.23724504 -1.0891301   2.05195079 -0.50750906
  1.38318062  2.14596688  0.30541948 -0.82402836  0.93685122 -0.10893151
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  0.9336318  -1.14157368  0.47936457  1.96423159  1.13698885  1.8718678
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Unique values in p90r_300: [-7.38980930e-01 -1.54835424e-01 -1.56248425e-01 -7.12228548e-01
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Unique values in p00r_300: [-6.75829057e-01 -1.93095414e-01 -1.11893739e-01 -6.38663700e-01
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Unique values in p10r_300: [-6.36833335e-01 -1.56917334e-01 -6.84671853e-02 -5.93155396e-01
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  5.89489111e-02 -4.00698232e-01  8.26728298e-01 -5.64129893e-01
 -5.85115001e-01 -3.76048979e-01 -5.68442310e-01 -7.13464791e-01
 -5.87488405e-01  2.52204215e+00 -5.63873734e-01 -4.85238024e-01
  3.86382401e+00 -7.08130226e-01  2.21016880e-01 -4.69117442e-01
 -6.32709928e-01 -6.76132538e-01  9.34930147e-02 -3.05938491e-02
 -4.33266847e-01 -7.26653062e-01  1.20600880e+00 -4.70164460e-01
 -3.13465044e-01 -5.41728867e-01 -3.37630166e-01 -3.91954022e-01
 -3.76043176e-01 -4.24672685e-01 -6.27628902e-02 -2.17238120e-01
 -1.08076431e-01 -4.18556381e-01 -4.72827182e-01  3.29823894e-02
 -4.12812294e-01 -6.82678260e-01 -1.31973788e-01 -9.73874061e-02
 -3.05192398e-02 -7.30282058e-01  8.75242711e-02 -5.06098783e-01
 -3.31725254e-01  1.78969389e+00 -1.90567650e-02  2.38113006e+00
  4.79936004e-01 -3.43806157e-01  6.90682393e-01  3.41504910e+00
  4.81593988e-01  1.53057893e-02 -5.82623879e-01 -5.80949315e-01
 -1.17696056e-01 -2.44404193e-01 -3.41583629e-01 -7.04612481e-01
  1.86391669e-02 -7.53138284e-01 -1.63750716e-01  1.13728714e-01
  9.33461040e-01 -7.10850398e-01 -5.02447073e-01 -4.65452468e-01
 -6.61077211e-01 -3.92122308e-01 -4.88256385e-01 -5.02754629e-01
 -6.67470399e-01 -3.60421647e-01 -1.13482289e-01 -6.18088993e-01
 -1.06223634e-01  7.72437601e-01 -1.71756294e-01  2.21078046e+00
 -4.37206218e-01 -1.99622036e-01 -2.87220810e-01 -4.88369957e-01]
Unique values in Country_encoded: [ 0.60568138  0.51463124 -1.67057217  1.06093209 -1.9437226  -1.03322118
  0.15043067 -1.57952203 -1.12427132  0.96988195  0.69673152 -0.48692033
  0.24148081 -0.12271976 -1.48847189 -0.76007075 -0.66902061 -1.39742175
  0.05938053 -0.03166961  0.87883181  0.78778166 -1.76162231 -1.3063716
 -0.57797047 -0.30482004 -0.85112089  0.4235811  -0.39587018  0.33253095
 -0.2137699  -1.21532146 -1.85267246 -0.94217104]
In [18]:
# Plot histograms for Latitude and Longitude
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.hist(df['Latitude'], bins=20)
plt.xlabel('Latitude')
plt.ylabel('Frequency')
plt.subplot(1, 2, 2)
plt.hist(df['Longitude'], bins=20)
plt.xlabel('Longitude')
plt.ylabel('Frequency')
plt.tight_layout()
plt.show()
In [19]:
# Calculate mean, median, and standard deviation for p90_1200, p00_1200, and p10_1200
statistics_1200 = df[['p90_1200', 'p00_1200', 'p10_1200']].describe()
statistics_300 = df[['p90_300', 'p00_300', 'p10_300']].describe()

# Create a DataFrame to compare statistics across time intervals
comparison_stats = pd.concat([statistics_1200, statistics_300], keys=['1200', '300'])
comparison_stats = comparison_stats.rename(index={'1200': 'Statistics for 1200', '300': 'Statistics for 300'})
print(comparison_stats)
                               p90_1200      p00_1200      p10_1200  \
Statistics for 1200 count  2.760000e+02  2.760000e+02  2.760000e+02   
                    mean  -5.792468e-17 -1.287215e-17  1.930823e-17   
                    std    1.001817e+00  1.001817e+00  1.001817e+00   
                    min   -1.606461e+00 -1.536982e+00 -1.498838e+00   
                    25%   -7.151712e-01 -7.026149e-01 -6.833262e-01   
                    50%   -3.670322e-01 -3.666486e-01 -3.975284e-01   
                    75%    7.049230e-01  5.991319e-01  5.246408e-01   
                    max    3.440412e+00  3.963610e+00  4.471422e+00   
Statistics for 300  count           NaN           NaN           NaN   
                    mean            NaN           NaN           NaN   
                    std             NaN           NaN           NaN   
                    min             NaN           NaN           NaN   
                    25%             NaN           NaN           NaN   
                    50%             NaN           NaN           NaN   
                    75%             NaN           NaN           NaN   
                    max             NaN           NaN           NaN   

                                p90_300       p00_300       p10_300  
Statistics for 1200 count           NaN           NaN           NaN  
                    mean            NaN           NaN           NaN  
                    std             NaN           NaN           NaN  
                    min             NaN           NaN           NaN  
                    25%             NaN           NaN           NaN  
                    50%             NaN           NaN           NaN  
                    75%             NaN           NaN           NaN  
                    max             NaN           NaN           NaN  
Statistics for 300  count  2.760000e+02  2.760000e+02  2.760000e+02  
                    mean   8.366898e-17 -1.287215e-17 -1.351576e-16  
                    std    1.001817e+00  1.001817e+00  1.001817e+00  
                    min   -1.401155e+00 -1.351009e+00 -1.335402e+00  
                    25%   -7.848346e-01 -7.573392e-01 -7.504482e-01  
                    50%   -2.745483e-01 -2.833819e-01 -2.697757e-01  
                    75%    6.557133e-01  5.757628e-01  5.839749e-01  
                    max    3.903643e+00  4.980302e+00  5.563019e+00  
In [20]:
# Scatter plot of Latitude and Longitude on a map

plt.figure(figsize=(10, 8))
plt.scatter(df['Longitude'], df['Latitude'])
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Geographic Distribution')
plt.show()
In [21]:
# Select variables for correlation analysis
variables = ['p90_1200', 'p00_1200', 'p10_1200', 'Latitude', 'Longitude']

# Compute correlation matrix
corr_matrix = df[variables].corr()

# Plot correlation matrix as a heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix for Multiple Variables')
plt.show()
In [22]:
# Create a scatter plot of p90_1200 vs. Longitude with color encoding for Latitude
plt.scatter(df['Longitude'], df['p90_1200'], c=df['Latitude'], cmap='viridis')
plt.colorbar(label='Latitude')
plt.xlabel('Longitude')
plt.ylabel('p90_1200')
plt.title('Scatter Plot of p90_1200 vs. Longitude with Color Encoding for Latitude')
plt.show()
In [23]:
df = df.drop("Country", axis=1)
df
Out[23]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
0 1.352765 0.251194 -0.464402 -0.508016 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 -0.282518 ... -1.172443 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829 -0.636833 0.605681
1 -0.126329 -0.064588 -0.774040 -0.731805 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 -0.404765 ... -0.586885 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095 -0.156917 0.514631
2 -4.916056 -0.579314 -1.002907 -0.916580 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 -0.576372 ... 0.414370 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894 -0.068467 -1.670572
3 -0.469286 -1.226995 -0.889115 -0.813688 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 -0.655143 ... -1.145335 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664 -0.593155 1.060932
4 -0.020197 0.018589 0.051419 0.029799 0.051246 0.176309 0.148003 0.179467 -0.085040 -0.085873 ... -0.741186 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095 -0.473250 0.514631
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 -1.506439 1.501828 1.792834 1.899641 1.913771 0.839405 1.051898 1.110167 2.454943 2.384831 ... 1.727114 2.251499 2.276462 1.196198 1.746700 1.828051 2.090747 2.335818 2.210780 -1.397422
272 0.096301 -0.957393 -0.854920 -0.820254 -0.771264 -0.789761 -0.786267 -0.743154 -0.772925 -0.718363 ... 0.245594 0.188036 0.253678 0.620326 0.569365 0.671899 -0.509843 -0.477951 -0.437206 1.060932
273 -0.461599 1.689799 2.357024 2.340021 2.270520 2.538004 2.634711 2.644594 1.762702 1.682191 ... 0.566912 0.570582 0.541617 0.867854 0.941671 0.931923 -0.165667 -0.201114 -0.199622 -0.395870
274 0.461080 0.470339 0.061055 -0.037393 -0.131383 -0.141744 -0.234249 -0.340844 0.257537 0.153308 ... -0.455104 -0.551699 -0.640980 -0.537094 -0.643647 -0.744355 -0.153133 -0.229645 -0.287221 0.878832
275 0.074844 -1.154910 -0.616189 -0.577300 -0.524737 -0.479518 -0.452401 -0.391292 -0.648693 -0.600271 ... -0.497452 -0.464913 -0.420298 -0.360175 -0.330085 -0.282576 -0.574150 -0.530945 -0.488370 1.060932

276 rows × 57 columns

In [24]:
# Get all the features (column names)
features = df.columns.tolist()

# Print the list of features
print("All Features:", features)
All Features: ['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']
In [25]:
from scipy.stats import chi2_contingency
from itertools import combinations
# Perform chi-square tests for all possible combinations of features
def perform_chi_square_tests(data, features):
    results = []
    for feature1, feature2 in combinations(features, 2):
        contingency_table = pd.crosstab(data[feature1], data[feature2])
        chi2, p_value, _, _ = chi2_contingency(contingency_table)
        results.append((feature1, feature2, chi2, p_value))
    return results

# Specify the list of features for the chi-square tests
features =['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']

# Perform the chi-square tests
chi_square_results = perform_chi_square_tests(df, features)

# Print the results
for result in chi_square_results:
    feature1, feature2, chi2_statistic, p_value = result
    print("Chi-square test between", feature1, "and", feature2)
    print("Chi-square statistic:", chi2_statistic)
    print("P-value:", p_value)
    print()
Chi-square test between Latitude and Longitude
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p90_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between Latitude and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between Latitude and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between Longitude and p90_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10u_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_1200
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p00r_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_30
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p00u_30
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p10u_30
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between Longitude and p90r_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_75
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between Longitude and p10_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_75
Chi-square statistic: 74796.00000000001
P-value: 0.24432170848750906

Chi-square test between Longitude and p00u_75
Chi-square statistic: 74796.00000000001
P-value: 0.24432170848750906

Chi-square test between Longitude and p10u_75
Chi-square statistic: 74796.00000000001
P-value: 0.24432170848750906

Chi-square test between Longitude and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_150
Chi-square statistic: 75347.99999999999
P-value: 0.24028111743839922

Chi-square test between Longitude and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_150
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_600
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between Longitude and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.22970721827216425

Chi-square test between p90_1200 and p00_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p00_1200 and p10_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_1200
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_1200 and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_1200
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90u_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90u_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90u_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00u_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00u_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00u_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00u_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p10u_1200 and p90r_1200
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10u_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10u_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10u_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p10u_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_1200 and p00r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_30
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p00u_30
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p10u_30
Chi-square statistic: 75348.00000000001
P-value: 0.2410841445243976

Chi-square test between p90r_1200 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90r_1200 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90r_1200 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90r_1200 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00u_150
Chi-square statistic: 75348.00000000001
P-value: 0.2410841445243976

Chi-square test between p90r_1200 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.31045659585425467

Chi-square test between p00r_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_30 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_30 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_30 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_30 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_30 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_30 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_30 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_30 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_30 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p10u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_30 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.0789004181855964

Chi-square test between p90u_30 and p10u_30
Chi-square statistic: 75624.0
P-value: 0.0789004181855964

Chi-square test between p90u_30 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90u_30 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_30 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_30 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_30 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90u_30 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90u_30 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.31045659585425944

Chi-square test between p00u_30 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.07890041818559086

Chi-square test between p00u_30 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_30 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_30 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_30 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_30 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_30 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_30 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.31045659585425944

Chi-square test between p10u_30 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p10u_30 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_30 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_30 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_30 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p10u_30 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00u_600
Chi-square statistic: 75348.00000000001
P-value: 0.2410841445243976

Chi-square test between p10u_30 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.31045659585425944

Chi-square test between p90r_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90r_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90r_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90r_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and Country_encoded
Chi-square statistic: 9107.999999999998
P-value: 0.40144847024692004

Chi-square test between p00r_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_75 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_75 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_75 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_75 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.0792797328485861

Chi-square test between p00_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.0792797328485861

Chi-square test between p00_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.0792797328485861

Chi-square test between p00_75 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00_75 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00_75 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.31045659585425467

Chi-square test between p10_75 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_75 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.017201147564985513

Chi-square test between p90u_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.017201147564985513

Chi-square test between p90u_75 and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00u_150
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between p90u_75 and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00u_600
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_75 and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.22970721827216425

Chi-square test between p00u_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.017201147564985513

Chi-square test between p00u_75 and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00u_150
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_75 and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00u_600
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_75 and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.22970721827216425

Chi-square test between p10u_75 and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00u_150
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_75 and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00u_600
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between p10u_75 and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.22970721827216012

Chi-square test between p90r_75 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_150 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_150 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_150 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_150 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00u_150 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_150 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and Country_encoded
Chi-square statistic: 9078.776470588236
P-value: 0.3904815408394916

Chi-square test between p10u_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_600 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_600 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_600 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00u_600 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and Country_encoded
Chi-square statistic: 9096.519327731094
P-value: 0.34106391374156725

Chi-square test between p10u_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p00r_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p00r_600 and p10r_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p00r_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_300 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_300 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p10_300 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p00u_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p10_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_300 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p00u_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10u_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

In [26]:
# Specify the list of features for the chi-square tests
features = ['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']

# Perform the chi-square test for independence
def perform_chi_square_tests(data):
    results = []
    columns = data.columns
    for col1, col2 in combinations(columns, 2):
        contingency_table = pd.crosstab(data[col1], data[col2])
        chi2, p_value, _, _ = chi2_contingency(contingency_table)
        results.append((col1, col2, chi2, p_value))
    return results

# Perform the chi-square tests for independence
chi_square_results = perform_chi_square_tests(df)

# Sort the results based on p-values in ascending order
chi_square_results.sort(key=lambda x: x[3])

# Identify the independent and dependent variables based on p-values
independent_variables = []
dependent_variables = []

for result in chi_square_results:
    feature1, feature2, _, p_value = result
    if p_value < 0.05:  # Set the desired significance level
        dependent_variables.append((feature1, feature2))
    else:
        independent_variables.append((feature1, feature2))

# Print the independent and dependent variables
print("Independent variables:")
for pair in independent_variables:
    print(pair[0], "and", pair[1])

print("\nDependent variables:")
for pair in dependent_variables:
    print(pair[0], "and", pair[1])
Independent variables:
p00u_30 and p10u_30
p90u_30 and p00u_30
p90u_30 and p10u_30
p00_75 and p90u_75
p00_75 and p00u_75
p00_75 and p10u_75
p10u_75 and Country_encoded
Longitude and Country_encoded
p90u_75 and Country_encoded
p00u_75 and Country_encoded
Latitude and p90_1200
Latitude and p00_1200
Latitude and p10_1200
Latitude and p90u_1200
Latitude and p00u_1200
Latitude and p10u_1200
Latitude and p00r_1200
Latitude and p10r_1200
Latitude and p90_30
Latitude and p00_30
Latitude and p10_30
Latitude and p90r_30
Latitude and p00r_30
Latitude and p10r_30
Latitude and p90_75
Latitude and p10_75
Latitude and p90r_75
Latitude and p00r_75
Latitude and p10r_75
Latitude and p90_150
Latitude and p00_150
Latitude and p10_150
Latitude and p90u_150
Latitude and p10u_150
Latitude and p90r_150
Latitude and p00r_150
Latitude and p10r_150
Latitude and p90_600
Latitude and p00_600
Latitude and p10_600
Latitude and p90u_600
Latitude and p10u_600
Latitude and p90r_600
Latitude and p00r_600
Latitude and p10r_600
Latitude and p90_300
Latitude and p00_300
Latitude and p10_300
Latitude and p90u_300
Latitude and p00u_300
Latitude and p10u_300
Latitude and p90r_300
Latitude and p00r_300
Latitude and p10r_300
p90_1200 and p00_1200
p90_1200 and p10_1200
p90_1200 and p90u_1200
p90_1200 and p00u_1200
p90_1200 and p10u_1200
p90_1200 and p00r_1200
p90_1200 and p10r_1200
p90_1200 and p90_30
p90_1200 and p00_30
p90_1200 and p10_30
p90_1200 and p90r_30
p90_1200 and p00r_30
p90_1200 and p10r_30
p90_1200 and p90_75
p90_1200 and p10_75
p90_1200 and p90r_75
p90_1200 and p00r_75
p90_1200 and p10r_75
p90_1200 and p90_150
p90_1200 and p00_150
p90_1200 and p10_150
p90_1200 and p90u_150
p90_1200 and p10u_150
p90_1200 and p90r_150
p90_1200 and p00r_150
p90_1200 and p10r_150
p90_1200 and p90_600
p90_1200 and p00_600
p90_1200 and p10_600
p90_1200 and p90u_600
p90_1200 and p10u_600
p90_1200 and p90r_600
p90_1200 and p00r_600
p90_1200 and p10r_600
p90_1200 and p90_300
p90_1200 and p00_300
p90_1200 and p10_300
p90_1200 and p90u_300
p90_1200 and p00u_300
p90_1200 and p10u_300
p90_1200 and p90r_300
p90_1200 and p00r_300
p90_1200 and p10r_300
p00_1200 and p10_1200
p00_1200 and p90u_1200
p00_1200 and p00u_1200
p00_1200 and p10u_1200
p00_1200 and p00r_1200
p00_1200 and p10r_1200
p00_1200 and p90_30
p00_1200 and p00_30
p00_1200 and p10_30
p00_1200 and p90r_30
p00_1200 and p00r_30
p00_1200 and p10r_30
p00_1200 and p90_75
p00_1200 and p10_75
p00_1200 and p90r_75
p00_1200 and p00r_75
p00_1200 and p10r_75
p00_1200 and p90_150
p00_1200 and p00_150
p00_1200 and p10_150
p00_1200 and p90u_150
p00_1200 and p10u_150
p00_1200 and p90r_150
p00_1200 and p00r_150
p00_1200 and p10r_150
p00_1200 and p90_600
p00_1200 and p00_600
p00_1200 and p10_600
p00_1200 and p90u_600
p00_1200 and p10u_600
p00_1200 and p90r_600
p00_1200 and p00r_600
p00_1200 and p10r_600
p00_1200 and p90_300
p00_1200 and p00_300
p00_1200 and p10_300
p00_1200 and p90u_300
p00_1200 and p00u_300
p00_1200 and p10u_300
p00_1200 and p90r_300
p00_1200 and p00r_300
p00_1200 and p10r_300
p10_1200 and p90u_1200
p10_1200 and p00u_1200
p10_1200 and p10u_1200
p10_1200 and p00r_1200
p10_1200 and p10r_1200
p10_1200 and p90_30
p10_1200 and p00_30
p10_1200 and p10_30
p10_1200 and p90r_30
p10_1200 and p00r_30
p10_1200 and p10r_30
p10_1200 and p90_75
p10_1200 and p10_75
p10_1200 and p90r_75
p10_1200 and p00r_75
p10_1200 and p10r_75
p10_1200 and p90_150
p10_1200 and p00_150
p10_1200 and p10_150
p10_1200 and p90u_150
p10_1200 and p10u_150
p10_1200 and p90r_150
p10_1200 and p00r_150
p10_1200 and p10r_150
p10_1200 and p90_600
p10_1200 and p00_600
p10_1200 and p10_600
p10_1200 and p90u_600
p10_1200 and p10u_600
p10_1200 and p90r_600
p10_1200 and p00r_600
p10_1200 and p10r_600
p10_1200 and p90_300
p10_1200 and p00_300
p10_1200 and p10_300
p10_1200 and p90u_300
p10_1200 and p00u_300
p10_1200 and p10u_300
p10_1200 and p90r_300
p10_1200 and p00r_300
p10_1200 and p10r_300
p90u_1200 and p00u_1200
p90u_1200 and p10u_1200
p90u_1200 and p00r_1200
p90u_1200 and p10r_1200
p90u_1200 and p90_30
p90u_1200 and p00_30
p90u_1200 and p10_30
p90u_1200 and p90r_30
p90u_1200 and p00r_30
p90u_1200 and p10r_30
p90u_1200 and p90_75
p90u_1200 and p10_75
p90u_1200 and p90r_75
p90u_1200 and p00r_75
p90u_1200 and p10r_75
p90u_1200 and p90_150
p90u_1200 and p00_150
p90u_1200 and p10_150
p90u_1200 and p90u_150
p90u_1200 and p10u_150
p90u_1200 and p90r_150
p90u_1200 and p00r_150
p90u_1200 and p10r_150
p90u_1200 and p90_600
p90u_1200 and p00_600
p90u_1200 and p10_600
p90u_1200 and p90u_600
p90u_1200 and p10u_600
p90u_1200 and p90r_600
p90u_1200 and p00r_600
p90u_1200 and p10r_600
p90u_1200 and p90_300
p90u_1200 and p00_300
p90u_1200 and p10_300
p90u_1200 and p90u_300
p90u_1200 and p00u_300
p90u_1200 and p10u_300
p90u_1200 and p90r_300
p90u_1200 and p00r_300
p90u_1200 and p10r_300
p00u_1200 and p10u_1200
p00u_1200 and p00r_1200
p00u_1200 and p10r_1200
p00u_1200 and p90_30
p00u_1200 and p00_30
p00u_1200 and p10_30
p00u_1200 and p90r_30
p00u_1200 and p00r_30
p00u_1200 and p10r_30
p00u_1200 and p90_75
p00u_1200 and p10_75
p00u_1200 and p90r_75
p00u_1200 and p00r_75
p00u_1200 and p10r_75
p00u_1200 and p90_150
p00u_1200 and p00_150
p00u_1200 and p10_150
p00u_1200 and p90u_150
p00u_1200 and p10u_150
p00u_1200 and p90r_150
p00u_1200 and p00r_150
p00u_1200 and p10r_150
p00u_1200 and p90_600
p00u_1200 and p00_600
p00u_1200 and p10_600
p00u_1200 and p90u_600
p00u_1200 and p10u_600
p00u_1200 and p90r_600
p00u_1200 and p00r_600
p00u_1200 and p10r_600
p00u_1200 and p90_300
p00u_1200 and p00_300
p00u_1200 and p10_300
p00u_1200 and p90u_300
p00u_1200 and p00u_300
p00u_1200 and p10u_300
p00u_1200 and p90r_300
p00u_1200 and p00r_300
p00u_1200 and p10r_300
p10u_1200 and p00r_1200
p10u_1200 and p10r_1200
p10u_1200 and p90_30
p10u_1200 and p00_30
p10u_1200 and p10_30
p10u_1200 and p90r_30
p10u_1200 and p00r_30
p10u_1200 and p10r_30
p10u_1200 and p90_75
p10u_1200 and p10_75
p10u_1200 and p90r_75
p10u_1200 and p00r_75
p10u_1200 and p10r_75
p10u_1200 and p90_150
p10u_1200 and p00_150
p10u_1200 and p10_150
p10u_1200 and p90u_150
p10u_1200 and p10u_150
p10u_1200 and p90r_150
p10u_1200 and p00r_150
p10u_1200 and p10r_150
p10u_1200 and p90_600
p10u_1200 and p00_600
p10u_1200 and p10_600
p10u_1200 and p90u_600
p10u_1200 and p10u_600
p10u_1200 and p90r_600
p10u_1200 and p00r_600
p10u_1200 and p10r_600
p10u_1200 and p90_300
p10u_1200 and p00_300
p10u_1200 and p10_300
p10u_1200 and p90u_300
p10u_1200 and p00u_300
p10u_1200 and p10u_300
p10u_1200 and p90r_300
p10u_1200 and p00r_300
p00r_1200 and p10r_1200
p00r_1200 and p90_30
p00r_1200 and p00_30
p00r_1200 and p10_30
p00r_1200 and p90r_30
p00r_1200 and p00r_30
p00r_1200 and p10r_30
p00r_1200 and p90_75
p00r_1200 and p10_75
p00r_1200 and p90r_75
p00r_1200 and p00r_75
p00r_1200 and p10r_75
p00r_1200 and p90_150
p00r_1200 and p00_150
p00r_1200 and p10_150
p00r_1200 and p90u_150
p00r_1200 and p10u_150
p00r_1200 and p90r_150
p00r_1200 and p00r_150
p00r_1200 and p10r_150
p00r_1200 and p90_600
p00r_1200 and p00_600
p00r_1200 and p10_600
p00r_1200 and p90u_600
p00r_1200 and p10u_600
p00r_1200 and p90r_600
p00r_1200 and p00r_600
p00r_1200 and p10r_600
p00r_1200 and p90_300
p00r_1200 and p00_300
p00r_1200 and p10_300
p00r_1200 and p90u_300
p00r_1200 and p00u_300
p00r_1200 and p10u_300
p00r_1200 and p90r_300
p00r_1200 and p00r_300
p00r_1200 and p10r_300
p10r_1200 and p90_30
p10r_1200 and p00_30
p10r_1200 and p10_30
p10r_1200 and p90r_30
p10r_1200 and p00r_30
p10r_1200 and p10r_30
p10r_1200 and p90_75
p10r_1200 and p10_75
p10r_1200 and p90r_75
p10r_1200 and p00r_75
p10r_1200 and p10r_75
p10r_1200 and p90_150
p10r_1200 and p00_150
p10r_1200 and p10_150
p10r_1200 and p90u_150
p10r_1200 and p10u_150
p10r_1200 and p90r_150
p10r_1200 and p00r_150
p10r_1200 and p10r_150
p10r_1200 and p90_600
p10r_1200 and p00_600
p10r_1200 and p10_600
p10r_1200 and p90u_600
p10r_1200 and p10u_600
p10r_1200 and p90r_600
p10r_1200 and p00r_600
p10r_1200 and p10r_600
p10r_1200 and p90_300
p10r_1200 and p00_300
p10r_1200 and p10_300
p10r_1200 and p90u_300
p10r_1200 and p00u_300
p10r_1200 and p10u_300
p10r_1200 and p90r_300
p10r_1200 and p00r_300
p10r_1200 and p10r_300
p90_30 and p00_30
p90_30 and p10_30
p90_30 and p90r_30
p90_30 and p00r_30
p90_30 and p10r_30
p90_30 and p90_75
p90_30 and p10_75
p90_30 and p90r_75
p90_30 and p00r_75
p90_30 and p10r_75
p90_30 and p90_150
p90_30 and p00_150
p90_30 and p10_150
p90_30 and p90u_150
p90_30 and p10u_150
p90_30 and p90r_150
p90_30 and p00r_150
p90_30 and p10r_150
p90_30 and p90_600
p90_30 and p00_600
p90_30 and p10_600
p90_30 and p90u_600
p90_30 and p10u_600
p90_30 and p90r_600
p90_30 and p00r_600
p90_30 and p10r_600
p90_30 and p90_300
p90_30 and p00_300
p90_30 and p10_300
p90_30 and p90u_300
p90_30 and p00u_300
p90_30 and p10u_300
p90_30 and p90r_300
p90_30 and p00r_300
p90_30 and p10r_300
p00_30 and p10_30
p00_30 and p90r_30
p00_30 and p00r_30
p00_30 and p10r_30
p00_30 and p90_75
p00_30 and p10_75
p00_30 and p90r_75
p00_30 and p00r_75
p00_30 and p10r_75
p00_30 and p90_150
p00_30 and p00_150
p00_30 and p10_150
p00_30 and p90u_150
p00_30 and p10u_150
p00_30 and p90r_150
p00_30 and p00r_150
p00_30 and p10r_150
p00_30 and p90_600
p00_30 and p00_600
p00_30 and p10_600
p00_30 and p90u_600
p00_30 and p10u_600
p00_30 and p90r_600
p00_30 and p00r_600
p00_30 and p10r_600
p00_30 and p90_300
p00_30 and p00_300
p00_30 and p10_300
p00_30 and p90u_300
p00_30 and p00u_300
p00_30 and p10u_300
p00_30 and p90r_300
p00_30 and p00r_300
p00_30 and p10r_300
p10_30 and p90r_30
p10_30 and p00r_30
p10_30 and p10r_30
p10_30 and p90_75
p10_30 and p10_75
p10_30 and p90r_75
p10_30 and p00r_75
p10_30 and p10r_75
p10_30 and p90_150
p10_30 and p00_150
p10_30 and p10_150
p10_30 and p90u_150
p10_30 and p10u_150
p10_30 and p90r_150
p10_30 and p00r_150
p10_30 and p10r_150
p10_30 and p90_600
p10_30 and p00_600
p10_30 and p10_600
p10_30 and p90u_600
p10_30 and p10u_600
p10_30 and p90r_600
p10_30 and p00r_600
p10_30 and p10r_600
p10_30 and p90_300
p10_30 and p00_300
p10_30 and p10_300
p10_30 and p90u_300
p10_30 and p00u_300
p10_30 and p10u_300
p10_30 and p90r_300
p10_30 and p00r_300
p10_30 and p10r_300
p90r_30 and p00r_30
p90r_30 and p10r_30
p90r_30 and p90_75
p90r_30 and p10_75
p90r_30 and p90r_75
p90r_30 and p00r_75
p90r_30 and p10r_75
p90r_30 and p90_150
p90r_30 and p00_150
p90r_30 and p10_150
p90r_30 and p90u_150
p90r_30 and p10u_150
p90r_30 and p90r_150
p90r_30 and p00r_150
p90r_30 and p10r_150
p90r_30 and p90_600
p90r_30 and p00_600
p90r_30 and p10_600
p90r_30 and p90u_600
p90r_30 and p10u_600
p90r_30 and p90r_600
p90r_30 and p00r_600
p90r_30 and p10r_600
p90r_30 and p90_300
p90r_30 and p00_300
p90r_30 and p10_300
p90r_30 and p90u_300
p90r_30 and p00u_300
p90r_30 and p10u_300
p90r_30 and p90r_300
p90r_30 and p00r_300
p90r_30 and p10r_300
p00r_30 and p10r_30
p00r_30 and p90_75
p00r_30 and p10_75
p00r_30 and p90r_75
p00r_30 and p00r_75
p00r_30 and p10r_75
p00r_30 and p90_150
p00r_30 and p00_150
p00r_30 and p10_150
p00r_30 and p90u_150
p00r_30 and p10u_150
p00r_30 and p90r_150
p00r_30 and p00r_150
p00r_30 and p10r_150
p00r_30 and p90_600
p00r_30 and p00_600
p00r_30 and p10_600
p00r_30 and p90u_600
p00r_30 and p10u_600
p00r_30 and p90r_600
p00r_30 and p00r_600
p00r_30 and p10r_600
p00r_30 and p90_300
p00r_30 and p00_300
p00r_30 and p10_300
p00r_30 and p90u_300
p00r_30 and p00u_300
p00r_30 and p10u_300
p00r_30 and p90r_300
p00r_30 and p00r_300
p00r_30 and p10r_300
p10r_30 and p90_75
p10r_30 and p10_75
p10r_30 and p90r_75
p10r_30 and p00r_75
p10r_30 and p10r_75
p10r_30 and p90_150
p10r_30 and p00_150
p10r_30 and p10_150
p10r_30 and p90u_150
p10r_30 and p10u_150
p10r_30 and p90r_150
p10r_30 and p00r_150
p10r_30 and p10r_150
p10r_30 and p90_600
p10r_30 and p00_600
p10r_30 and p10_600
p10r_30 and p90u_600
p10r_30 and p10u_600
p10r_30 and p90r_600
p10r_30 and p00r_600
p10r_30 and p10r_600
p10r_30 and p90_300
p10r_30 and p00_300
p10r_30 and p10_300
p10r_30 and p90u_300
p10r_30 and p00u_300
p10r_30 and p10u_300
p10r_30 and p90r_300
p10r_30 and p00r_300
p10r_30 and p10r_300
p90_75 and p10_75
p90_75 and p90r_75
p90_75 and p00r_75
p90_75 and p10r_75
p90_75 and p90_150
p90_75 and p00_150
p90_75 and p10_150
p90_75 and p90u_150
p90_75 and p10u_150
p90_75 and p90r_150
p90_75 and p00r_150
p90_75 and p10r_150
p90_75 and p90_600
p90_75 and p00_600
p90_75 and p10_600
p90_75 and p90u_600
p90_75 and p10u_600
p90_75 and p90r_600
p90_75 and p00r_600
p90_75 and p10r_600
p90_75 and p90_300
p90_75 and p00_300
p90_75 and p10_300
p90_75 and p90u_300
p90_75 and p00u_300
p90_75 and p10u_300
p90_75 and p90r_300
p90_75 and p00r_300
p90_75 and p10r_300
p10_75 and p90r_75
p10_75 and p00r_75
p10_75 and p10r_75
p10_75 and p90_150
p10_75 and p00_150
p10_75 and p10_150
p10_75 and p90u_150
p10_75 and p10u_150
p10_75 and p90r_150
p10_75 and p00r_150
p10_75 and p10r_150
p10_75 and p90_600
p10_75 and p00_600
p10_75 and p10_600
p10_75 and p90u_600
p10_75 and p10u_600
p10_75 and p90r_600
p10_75 and p00r_600
p10_75 and p10r_600
p10_75 and p90_300
p10_75 and p00_300
p10_75 and p10_300
p10_75 and p90u_300
p10_75 and p00u_300
p10_75 and p10u_300
p10_75 and p90r_300
p10_75 and p00r_300
p10_75 and p10r_300
p90r_75 and p00r_75
p90r_75 and p10r_75
p90r_75 and p90_150
p90r_75 and p00_150
p90r_75 and p10_150
p90r_75 and p90u_150
p90r_75 and p10u_150
p90r_75 and p90r_150
p90r_75 and p00r_150
p90r_75 and p10r_150
p90r_75 and p90_600
p90r_75 and p00_600
p90r_75 and p10_600
p90r_75 and p90u_600
p90r_75 and p10u_600
p90r_75 and p90r_600
p90r_75 and p00r_600
p90r_75 and p10r_600
p90r_75 and p90_300
p90r_75 and p00_300
p90r_75 and p10_300
p90r_75 and p90u_300
p90r_75 and p00u_300
p90r_75 and p10u_300
p90r_75 and p90r_300
p90r_75 and p00r_300
p90r_75 and p10r_300
p00r_75 and p10r_75
p00r_75 and p90_150
p00r_75 and p00_150
p00r_75 and p10_150
p00r_75 and p90u_150
p00r_75 and p10u_150
p00r_75 and p90r_150
p00r_75 and p00r_150
p00r_75 and p10r_150
p00r_75 and p90_600
p00r_75 and p00_600
p00r_75 and p10_600
p00r_75 and p90u_600
p00r_75 and p10u_600
p00r_75 and p90r_600
p00r_75 and p00r_600
p00r_75 and p10r_600
p00r_75 and p90_300
p00r_75 and p00_300
p00r_75 and p10_300
p00r_75 and p90u_300
p00r_75 and p00u_300
p00r_75 and p10u_300
p00r_75 and p90r_300
p00r_75 and p00r_300
p00r_75 and p10r_300
p10r_75 and p90_150
p10r_75 and p00_150
p10r_75 and p10_150
p10r_75 and p90u_150
p10r_75 and p10u_150
p10r_75 and p90r_150
p10r_75 and p00r_150
p10r_75 and p10r_150
p10r_75 and p90_600
p10r_75 and p00_600
p10r_75 and p10_600
p10r_75 and p90u_600
p10r_75 and p10u_600
p10r_75 and p90r_600
p10r_75 and p00r_600
p10r_75 and p10r_600
p10r_75 and p90_300
p10r_75 and p00_300
p10r_75 and p10_300
p10r_75 and p90u_300
p10r_75 and p00u_300
p10r_75 and p10u_300
p10r_75 and p90r_300
p10r_75 and p00r_300
p10r_75 and p10r_300
p90_150 and p00_150
p90_150 and p10_150
p90_150 and p90u_150
p90_150 and p10u_150
p90_150 and p90r_150
p90_150 and p00r_150
p90_150 and p10r_150
p90_150 and p90_600
p90_150 and p00_600
p90_150 and p10_600
p90_150 and p90u_600
p90_150 and p10u_600
p90_150 and p90r_600
p90_150 and p00r_600
p90_150 and p10r_600
p90_150 and p90_300
p90_150 and p00_300
p90_150 and p10_300
p90_150 and p90u_300
p90_150 and p00u_300
p90_150 and p10u_300
p90_150 and p90r_300
p90_150 and p00r_300
p90_150 and p10r_300
p00_150 and p10_150
p00_150 and p90u_150
p00_150 and p10u_150
p00_150 and p90r_150
p00_150 and p00r_150
p00_150 and p10r_150
p00_150 and p90_600
p00_150 and p00_600
p00_150 and p10_600
p00_150 and p90u_600
p00_150 and p10u_600
p00_150 and p90r_600
p00_150 and p00r_600
p00_150 and p10r_600
p00_150 and p90_300
p00_150 and p00_300
p00_150 and p10_300
p00_150 and p90u_300
p00_150 and p00u_300
p00_150 and p10u_300
p00_150 and p90r_300
p00_150 and p00r_300
p00_150 and p10r_300
p10_150 and p90u_150
p10_150 and p10u_150
p10_150 and p90r_150
p10_150 and p00r_150
p10_150 and p10r_150
p10_150 and p90_600
p10_150 and p00_600
p10_150 and p10_600
p10_150 and p90u_600
p10_150 and p10u_600
p10_150 and p90r_600
p10_150 and p00r_600
p10_150 and p10r_600
p10_150 and p90_300
p10_150 and p00_300
p10_150 and p10_300
p10_150 and p90u_300
p10_150 and p00u_300
p10_150 and p10u_300
p10_150 and p90r_300
p10_150 and p00r_300
p10_150 and p10r_300
p90u_150 and p10u_150
p90u_150 and p90r_150
p90u_150 and p00r_150
p90u_150 and p10r_150
p90u_150 and p90_600
p90u_150 and p00_600
p90u_150 and p10_600
p90u_150 and p90u_600
p90u_150 and p10u_600
p90u_150 and p90r_600
p90u_150 and p00r_600
p90u_150 and p10r_600
p90u_150 and p90_300
p90u_150 and p00_300
p90u_150 and p10_300
p90u_150 and p90u_300
p90u_150 and p00u_300
p90u_150 and p10u_300
p90u_150 and p90r_300
p90u_150 and p00r_300
p90u_150 and p10r_300
p10u_150 and p90r_150
p10u_150 and p00r_150
p10u_150 and p10r_150
p10u_150 and p90_600
p10u_150 and p00_600
p10u_150 and p10_600
p10u_150 and p90u_600
p10u_150 and p10u_600
p10u_150 and p90r_600
p10u_150 and p00r_600
p10u_150 and p10r_600
p10u_150 and p90_300
p10u_150 and p00_300
p10u_150 and p10_300
p10u_150 and p90u_300
p10u_150 and p00u_300
p10u_150 and p10u_300
p10u_150 and p90r_300
p10u_150 and p00r_300
p10u_150 and p10r_300
p90r_150 and p00r_150
p90r_150 and p10r_150
p90r_150 and p90_600
p90r_150 and p00_600
p90r_150 and p10_600
p90r_150 and p90u_600
p90r_150 and p10u_600
p90r_150 and p90r_600
p90r_150 and p00r_600
p90r_150 and p10r_600
p90r_150 and p90_300
p90r_150 and p00_300
p90r_150 and p10_300
p90r_150 and p90u_300
p90r_150 and p00u_300
p90r_150 and p10u_300
p90r_150 and p90r_300
p90r_150 and p00r_300
p90r_150 and p10r_300
p00r_150 and p10r_150
p00r_150 and p90_600
p00r_150 and p00_600
p00r_150 and p10_600
p00r_150 and p90u_600
p00r_150 and p10u_600
p00r_150 and p90r_600
p00r_150 and p00r_600
p00r_150 and p10r_600
p00r_150 and p90_300
p00r_150 and p00_300
p00r_150 and p10_300
p00r_150 and p90u_300
p00r_150 and p00u_300
p00r_150 and p10u_300
p00r_150 and p90r_300
p00r_150 and p00r_300
p00r_150 and p10r_300
p10r_150 and p90_600
p10r_150 and p00_600
p10r_150 and p10_600
p10r_150 and p90u_600
p10r_150 and p10u_600
p10r_150 and p90r_600
p10r_150 and p00r_600
p10r_150 and p10r_600
p10r_150 and p90_300
p10r_150 and p00_300
p10r_150 and p10_300
p10r_150 and p90u_300
p10r_150 and p00u_300
p10r_150 and p10u_300
p10r_150 and p90r_300
p10r_150 and p00r_300
p10r_150 and p10r_300
p90_600 and p00_600
p90_600 and p10_600
p90_600 and p90u_600
p90_600 and p10u_600
p90_600 and p90r_600
p90_600 and p00r_600
p90_600 and p10r_600
p90_600 and p90_300
p90_600 and p00_300
p90_600 and p10_300
p90_600 and p90u_300
p90_600 and p00u_300
p90_600 and p10u_300
p90_600 and p90r_300
p90_600 and p00r_300
p90_600 and p10r_300
p00_600 and p10_600
p00_600 and p90u_600
p00_600 and p10u_600
p00_600 and p90r_600
p00_600 and p00r_600
p00_600 and p10r_600
p00_600 and p90_300
p00_600 and p00_300
p00_600 and p10_300
p00_600 and p90u_300
p00_600 and p00u_300
p00_600 and p10u_300
p00_600 and p90r_300
p00_600 and p00r_300
p00_600 and p10r_300
p10_600 and p90u_600
p10_600 and p10u_600
p10_600 and p90r_600
p10_600 and p00r_600
p10_600 and p10r_600
p10_600 and p90_300
p10_600 and p00_300
p10_600 and p10_300
p10_600 and p90u_300
p10_600 and p00u_300
p10_600 and p10u_300
p10_600 and p90r_300
p10_600 and p00r_300
p10_600 and p10r_300
p90u_600 and p10u_600
p90u_600 and p90r_600
p90u_600 and p00r_600
p90u_600 and p10r_600
p90u_600 and p90_300
p90u_600 and p00_300
p90u_600 and p10_300
p90u_600 and p90u_300
p90u_600 and p00u_300
p90u_600 and p10u_300
p90u_600 and p90r_300
p90u_600 and p00r_300
p90u_600 and p10r_300
p10u_600 and p90r_600
p10u_600 and p00r_600
p10u_600 and p10r_600
p10u_600 and p90_300
p10u_600 and p00_300
p10u_600 and p10_300
p10u_600 and p90u_300
p10u_600 and p00u_300
p10u_600 and p10u_300
p10u_600 and p90r_300
p10u_600 and p00r_300
p10u_600 and p10r_300
p90r_600 and p00r_600
p90r_600 and p10r_600
p90r_600 and p90_300
p90r_600 and p00_300
p90r_600 and p10_300
p90r_600 and p90u_300
p90r_600 and p00u_300
p90r_600 and p10u_300
p90r_600 and p90r_300
p90r_600 and p00r_300
p90r_600 and p10r_300
p00r_600 and p10r_600
p00r_600 and p90_300
p00r_600 and p00_300
p00r_600 and p10_300
p00r_600 and p90u_300
p00r_600 and p00u_300
p00r_600 and p10u_300
p00r_600 and p90r_300
p10r_600 and p90_300
p10r_600 and p00_300
p10r_600 and p10_300
p10r_600 and p90u_300
p10r_600 and p00u_300
p10r_600 and p10u_300
p10r_600 and p90r_300
p10r_600 and p00r_300
p10r_600 and p10r_300
p90_300 and p00_300
p90_300 and p10_300
p90_300 and p90u_300
p90_300 and p00u_300
p90_300 and p10u_300
p90_300 and p90r_300
p90_300 and p00r_300
p90_300 and p10r_300
p00_300 and p10_300
p00_300 and p90u_300
p00_300 and p00u_300
p00_300 and p10u_300
p00_300 and p90r_300
p00_300 and p00r_300
p00_300 and p10r_300
p10_300 and p90u_300
p10_300 and p10u_300
p10_300 and p90r_300
p10_300 and p00r_300
p10_300 and p10r_300
p90u_300 and p00u_300
p90u_300 and p10u_300
p90u_300 and p90r_300
p90u_300 and p00r_300
p90u_300 and p10r_300
p00u_300 and p10u_300
p00u_300 and p90r_300
p00u_300 and p00r_300
p00u_300 and p10r_300
p10u_300 and p90r_300
p10u_300 and p00r_300
p10u_300 and p10r_300
p90r_300 and p00r_300
p90r_300 and p10r_300
p00r_300 and p10r_300
p10u_1200 and p10r_300
p00r_600 and p00r_300
p00r_600 and p10r_300
p10_300 and p00u_300
Latitude and p90u_30
Latitude and p00u_30
Latitude and p10u_30
Latitude and p00_75
Latitude and p00u_150
p90_1200 and p90u_30
p90_1200 and p00u_30
p90_1200 and p10u_30
p90_1200 and p00u_600
p00_1200 and p90r_1200
p00_1200 and p10u_30
p00_1200 and p00u_150
p10_1200 and p90r_1200
p10_1200 and p00u_30
p10_1200 and p10u_30
p10_1200 and p00u_150
p10_1200 and p00u_600
p90u_1200 and p10u_30
p90u_1200 and p00_75
p90u_1200 and p00u_150
p00u_1200 and p90u_30
p00u_1200 and p00u_30
p00u_1200 and p10u_30
p00u_1200 and p00u_150
p10u_1200 and p90r_1200
p10u_1200 and p90u_30
p10u_1200 and p10u_30
p10u_1200 and p00_75
p10u_1200 and p00u_150
p10u_1200 and p00u_600
p00r_1200 and p90u_30
p00r_1200 and p00u_30
p00r_1200 and p10u_30
p00r_1200 and p00u_150
p10r_1200 and p90u_30
p10r_1200 and p00u_30
p10r_1200 and p10u_30
p10r_1200 and p00u_150
p90_30 and p10u_30
p90_30 and p00u_150
p90_30 and p00u_600
p00_30 and p00u_30
p00_30 and p10u_30
p00_30 and p00u_150
p10_30 and p00_75
p10_30 and p00u_150
p90r_30 and p00_75
p90r_30 and p00u_150
p00r_30 and p00u_150
p10r_30 and p00_75
p90_75 and p00u_150
p10_75 and p00u_150
p90r_75 and p00u_150
p90r_75 and p00u_600
p10r_75 and p00u_150
p90_150 and p00u_150
p00_150 and p00u_600
p90u_150 and p00u_150
p90r_150 and p00u_600
Latitude and p90r_1200
Latitude and p00u_600
p90_1200 and p90r_1200
p90_1200 and p00_75
p90_1200 and p00u_150
p00_1200 and p90u_30
p00_1200 and p00u_30
p00_1200 and p00_75
p00_1200 and p00u_600
p10_1200 and p90u_30
p10_1200 and p00_75
p90u_1200 and p90r_1200
p90u_1200 and p90u_30
p90u_1200 and p00u_30
p90u_1200 and p00u_600
p00u_1200 and p90r_1200
p00u_1200 and p00_75
p00u_1200 and p00u_600
p10u_1200 and p00u_30
p90r_1200 and p00r_1200
p90r_1200 and p10r_1200
p90r_1200 and p90_30
p90r_1200 and p00_30
p90r_1200 and p10_30
p90r_1200 and p90r_30
p90r_1200 and p00r_30
p90r_1200 and p10r_30
p90r_1200 and p90_75
p90r_1200 and p10_75
p90r_1200 and p90r_75
p90r_1200 and p00r_75
p90r_1200 and p10r_75
p90r_1200 and p90_150
p90r_1200 and p00_150
p90r_1200 and p10_150
p90r_1200 and p90u_150
p90r_1200 and p10u_150
p90r_1200 and p90r_150
p90r_1200 and p00r_150
p90r_1200 and p10r_150
p90r_1200 and p90_600
p90r_1200 and p00_600
p90r_1200 and p10_600
p90r_1200 and p90u_600
p90r_1200 and p10u_600
p90r_1200 and p90r_600
p90r_1200 and p00r_600
p90r_1200 and p10r_600
p90r_1200 and p90_300
p90r_1200 and p00_300
p90r_1200 and p10_300
p90r_1200 and p90u_300
p90r_1200 and p00u_300
p90r_1200 and p10u_300
p90r_1200 and p90r_300
p90r_1200 and p00r_300
p90r_1200 and p10r_300
p00r_1200 and p00_75
p00r_1200 and p00u_600
p10r_1200 and p00_75
p10r_1200 and p00u_600
p90_30 and p90u_30
p90_30 and p00u_30
p90_30 and p00_75
p00_30 and p90u_30
p00_30 and p00_75
p00_30 and p00u_600
p10_30 and p90u_30
p10_30 and p00u_30
p10_30 and p10u_30
p10_30 and p00u_600
p90u_30 and p90r_30
p90u_30 and p00r_30
p90u_30 and p10r_30
p90u_30 and p90_75
p90u_30 and p10_75
p90u_30 and p90r_75
p90u_30 and p00r_75
p90u_30 and p10r_75
p90u_30 and p90_150
p90u_30 and p00_150
p90u_30 and p10_150
p90u_30 and p90u_150
p90u_30 and p10u_150
p90u_30 and p90r_150
p90u_30 and p00r_150
p90u_30 and p10r_150
p90u_30 and p90_600
p90u_30 and p00_600
p90u_30 and p10_600
p90u_30 and p90u_600
p90u_30 and p10u_600
p90u_30 and p90r_600
p90u_30 and p00r_600
p90u_30 and p10r_600
p90u_30 and p90_300
p90u_30 and p00_300
p90u_30 and p10_300
p90u_30 and p90u_300
p90u_30 and p00u_300
p90u_30 and p10u_300
p90u_30 and p90r_300
p90u_30 and p00r_300
p90u_30 and p10r_300
p00u_30 and p90r_30
p00u_30 and p00r_30
p00u_30 and p10r_30
p00u_30 and p90_75
p00u_30 and p10_75
p00u_30 and p90r_75
p00u_30 and p00r_75
p00u_30 and p10r_75
p00u_30 and p90_150
p00u_30 and p00_150
p00u_30 and p10_150
p00u_30 and p90u_150
p00u_30 and p10u_150
p00u_30 and p90r_150
p00u_30 and p00r_150
p00u_30 and p10r_150
p00u_30 and p90_600
p00u_30 and p00_600
p00u_30 and p10_600
p00u_30 and p90u_600
p00u_30 and p10u_600
p00u_30 and p90r_600
p00u_30 and p00r_600
p00u_30 and p10r_600
p00u_30 and p90_300
p00u_30 and p00_300
p00u_30 and p10_300
p00u_30 and p90u_300
p00u_30 and p00u_300
p00u_30 and p10u_300
p00u_30 and p90r_300
p00u_30 and p00r_300
p00u_30 and p10r_300
p10u_30 and p90r_30
p10u_30 and p00r_30
p10u_30 and p10r_30
p10u_30 and p90_75
p10u_30 and p10_75
p10u_30 and p90r_75
p10u_30 and p00r_75
p10u_30 and p10r_75
p10u_30 and p90_150
p10u_30 and p00_150
p10u_30 and p10_150
p10u_30 and p90u_150
p10u_30 and p10u_150
p10u_30 and p90r_150
p10u_30 and p00r_150
p10u_30 and p10r_150
p10u_30 and p90_600
p10u_30 and p00_600
p10u_30 and p10_600
p10u_30 and p90u_600
p10u_30 and p10u_600
p10u_30 and p90r_600
p10u_30 and p00r_600
p10u_30 and p10r_600
p10u_30 and p90_300
p10u_30 and p00_300
p10u_30 and p10_300
p10u_30 and p90u_300
p10u_30 and p00u_300
p10u_30 and p10u_300
p10u_30 and p90r_300
p10u_30 and p00r_300
p10u_30 and p10r_300
p90r_30 and p00u_600
p00r_30 and p00_75
p00r_30 and p00u_600
p10r_30 and p00u_150
p10r_30 and p00u_600
p90_75 and p00_75
p90_75 and p00u_600
p00_75 and p10_75
p00_75 and p90r_75
p00_75 and p00r_75
p00_75 and p10r_75
p00_75 and p90_150
p00_75 and p00_150
p00_75 and p10_150
p00_75 and p90u_150
p00_75 and p10u_150
p00_75 and p90r_150
p00_75 and p00r_150
p00_75 and p10r_150
p00_75 and p90_600
p00_75 and p00_600
p00_75 and p10_600
p00_75 and p90u_600
p00_75 and p10u_600
p00_75 and p90r_600
p00_75 and p00r_600
p00_75 and p10r_600
p00_75 and p90_300
p00_75 and p00_300
p00_75 and p10_300
p00_75 and p90u_300
p00_75 and p00u_300
p00_75 and p10u_300
p00_75 and p90r_300
p00_75 and p00r_300
p00_75 and p10r_300
p10_75 and p00u_600
p00r_75 and p00u_150
p00r_75 and p00u_600
p10r_75 and p00u_600
p90_150 and p00u_600
p00_150 and p00u_150
p10_150 and p00u_150
p10_150 and p00u_600
p90u_150 and p00u_600
p00u_150 and p10u_150
p00u_150 and p90r_150
p00u_150 and p00r_150
p00u_150 and p10r_150
p00u_150 and p90_600
p00u_150 and p00_600
p00u_150 and p10_600
p00u_150 and p90u_600
p00u_150 and p10u_600
p00u_150 and p90r_600
p00u_150 and p00r_600
p00u_150 and p10r_600
p00u_150 and p90_300
p00u_150 and p00_300
p00u_150 and p10_300
p00u_150 and p90u_300
p00u_150 and p00u_300
p00u_150 and p10u_300
p00u_150 and p90r_300
p00u_150 and p00r_300
p00u_150 and p10r_300
p10u_150 and p00u_600
p00r_150 and p00u_600
p10r_150 and p00u_600
p90_600 and p00u_600
p00_600 and p00u_600
p10_600 and p00u_600
p90u_600 and p00u_600
p00u_600 and p10u_600
p00u_600 and p90r_600
p00u_600 and p00r_600
p00u_600 and p10r_600
p00u_600 and p90_300
p00u_600 and p00_300
p00u_600 and p10_300
p00u_600 and p90u_300
p00u_600 and p00u_300
p00u_600 and p10u_300
p00u_600 and p90r_300
p00u_600 and p00r_300
p00u_600 and p10r_300
Latitude and Longitude
Latitude and p90u_75
Latitude and p00u_75
Latitude and p10u_75
p90_1200 and p90u_75
p90_1200 and p00u_75
p90_1200 and p10u_75
p00_1200 and p90u_75
p00_1200 and p00u_75
p00_1200 and p10u_75
p10_1200 and p90u_75
p10_1200 and p00u_75
p10_1200 and p10u_75
p90u_1200 and p90u_75
p90u_1200 and p00u_75
p90u_1200 and p10u_75
p00u_1200 and p90u_75
p00u_1200 and p00u_75
p00u_1200 and p10u_75
p10u_1200 and p90u_75
p10u_1200 and p00u_75
p10u_1200 and p10u_75
p00r_1200 and p90u_75
p00r_1200 and p00u_75
p00r_1200 and p10u_75
p10r_1200 and p90u_75
p10r_1200 and p00u_75
p10r_1200 and p10u_75
p90_30 and p90u_75
p90_30 and p00u_75
p90_30 and p10u_75
p00_30 and p90u_75
p00_30 and p00u_75
p00_30 and p10u_75
p10_30 and p90u_75
p10_30 and p00u_75
p10_30 and p10u_75
p90r_30 and p90u_75
p90r_30 and p00u_75
p90r_30 and p10u_75
p00r_30 and p90u_75
p00r_30 and p00u_75
p00r_30 and p10u_75
p10r_30 and p90u_75
p10r_30 and p00u_75
p10r_30 and p10u_75
p90_75 and p90u_75
p90_75 and p00u_75
p90_75 and p10u_75
p10_75 and p90u_75
p10_75 and p00u_75
p10_75 and p10u_75
Longitude and p90_1200
Longitude and p00_1200
Longitude and p10_1200
Longitude and p90u_1200
Longitude and p00u_1200
Longitude and p10u_1200
Longitude and p00r_1200
Longitude and p10r_1200
Longitude and p90_30
Longitude and p00_30
Longitude and p10_30
Longitude and p90r_30
Longitude and p00r_30
Longitude and p10r_30
Longitude and p90_75
Longitude and p10_75
Longitude and p90r_75
Longitude and p00r_75
Longitude and p10r_75
Longitude and p90_150
Longitude and p10_150
Longitude and p90u_150
Longitude and p10u_150
Longitude and p90r_150
Longitude and p00r_150
Longitude and p10r_150
Longitude and p90_600
Longitude and p00_600
Longitude and p10_600
Longitude and p90u_600
Longitude and p10u_600
Longitude and p90r_600
Longitude and p00r_600
Longitude and p10r_600
Longitude and p90_300
Longitude and p00_300
Longitude and p10_300
Longitude and p90u_300
Longitude and p00u_300
Longitude and p10u_300
Longitude and p90r_300
Longitude and p00r_300
Longitude and p10r_300
p90u_75 and p90r_75
p90u_75 and p00r_75
p90u_75 and p10r_75
p90u_75 and p90_150
p90u_75 and p00_150
p90u_75 and p10_150
p90u_75 and p90u_150
p90u_75 and p10u_150
p90u_75 and p90r_150
p90u_75 and p00r_150
p90u_75 and p10r_150
p90u_75 and p90_600
p90u_75 and p00_600
p90u_75 and p10_600
p90u_75 and p90u_600
p90u_75 and p10u_600
p90u_75 and p90r_600
p90u_75 and p00r_600
p90u_75 and p10r_600
p90u_75 and p90_300
p90u_75 and p00_300
p90u_75 and p10_300
p90u_75 and p90u_300
p90u_75 and p00u_300
p90u_75 and p10u_300
p90u_75 and p90r_300
p90u_75 and p00r_300
p90u_75 and p10r_300
p00u_75 and p90r_75
p00u_75 and p00r_75
p00u_75 and p10r_75
p00u_75 and p90_150
p00u_75 and p00_150
p00u_75 and p10_150
p00u_75 and p90u_150
p00u_75 and p10u_150
p00u_75 and p90r_150
p00u_75 and p00r_150
p00u_75 and p10r_150
p00u_75 and p90_600
p00u_75 and p00_600
p00u_75 and p10_600
p00u_75 and p90u_600
p00u_75 and p10u_600
p00u_75 and p90r_600
p00u_75 and p00r_600
p00u_75 and p10r_600
p00u_75 and p90_300
p00u_75 and p00_300
p00u_75 and p10_300
p00u_75 and p90u_300
p00u_75 and p00u_300
p00u_75 and p10u_300
p00u_75 and p90r_300
p00u_75 and p00r_300
p00u_75 and p10r_300
p10u_75 and p90r_75
p10u_75 and p00r_75
p10u_75 and p10r_75
p10u_75 and p90_150
p10u_75 and p00_150
p10u_75 and p10_150
p10u_75 and p90u_150
p10u_75 and p10u_150
p10u_75 and p90r_150
p10u_75 and p00r_150
p10u_75 and p10r_150
p10u_75 and p90_600
p10u_75 and p00_600
p10u_75 and p10_600
p10u_75 and p90u_600
p10u_75 and p10u_600
p10u_75 and p90r_600
p10u_75 and p00r_600
p10u_75 and p10r_600
p10u_75 and p90_300
p10u_75 and p00_300
p10u_75 and p10_300
p10u_75 and p90u_300
p10u_75 and p00u_300
p10u_75 and p10u_300
p10u_75 and p90r_300
p10u_75 and p00r_300
p10u_75 and p10r_300
Longitude and p00_150
p90r_1200 and p10u_30
p90r_1200 and p00u_150
p10u_30 and p00u_600
p90r_1200 and p90u_30
p90r_1200 and p00u_30
p90r_1200 and p00_75
p90r_1200 and p00u_600
p90u_30 and p00_75
p90u_30 and p00u_150
p90u_30 and p00u_600
p00u_30 and p00_75
p00u_30 and p00u_150
p00u_30 and p00u_600
p10u_30 and p00_75
p10u_30 and p00u_150
p00_75 and p00u_150
p00_75 and p00u_600
p00u_150 and p00u_600
Longitude and p90r_1200
Longitude and p90u_30
Longitude and p00u_30
Longitude and p00u_150
p90r_1200 and p90u_75
p90r_1200 and p00u_75
p90r_1200 and p10u_75
p90u_30 and p90u_75
p90u_30 and p00u_75
p90u_30 and p10u_75
p00u_30 and p90u_75
p00u_30 and p00u_75
p00u_30 and p10u_75
p10u_30 and p90u_75
p10u_30 and p00u_75
p10u_30 and p10u_75
p90u_75 and p00u_600
p00u_75 and p00u_150
p00u_75 and p00u_600
p10u_75 and p00u_150
Longitude and p10u_30
Longitude and p00_75
Longitude and p00u_600
p90u_75 and p00u_150
p10u_75 and p00u_600
Longitude and p90u_75
Longitude and p00u_75
Longitude and p10u_75
p90r_1200 and Country_encoded
p00_75 and Country_encoded
p90u_30 and Country_encoded
p00u_30 and Country_encoded
p10u_30 and Country_encoded
p00u_600 and Country_encoded
p00u_150 and Country_encoded
p90_1200 and Country_encoded
p00u_1200 and Country_encoded
p00_300 and Country_encoded
p90u_300 and Country_encoded
Latitude and Country_encoded
p00_1200 and Country_encoded
p10_1200 and Country_encoded
p90u_1200 and Country_encoded
p10u_1200 and Country_encoded
p00r_1200 and Country_encoded
p10r_1200 and Country_encoded
p90_30 and Country_encoded
p00_30 and Country_encoded
p10_30 and Country_encoded
p00r_30 and Country_encoded
p10r_30 and Country_encoded
p90_75 and Country_encoded
p10_75 and Country_encoded
p90r_75 and Country_encoded
p00r_75 and Country_encoded
p10r_75 and Country_encoded
p90_150 and Country_encoded
p00_150 and Country_encoded
p10_150 and Country_encoded
p90u_150 and Country_encoded
p10u_150 and Country_encoded
p90r_150 and Country_encoded
p00r_150 and Country_encoded
p10r_150 and Country_encoded
p90_600 and Country_encoded
p00_600 and Country_encoded
p10_600 and Country_encoded
p90u_600 and Country_encoded
p10u_600 and Country_encoded
p90r_600 and Country_encoded
p00r_600 and Country_encoded
p10r_600 and Country_encoded
p90_300 and Country_encoded
p10_300 and Country_encoded
p00u_300 and Country_encoded
p10u_300 and Country_encoded
p90r_300 and Country_encoded
p00r_300 and Country_encoded
p10r_300 and Country_encoded
p90r_30 and Country_encoded

Dependent variables:
p90u_75 and p00u_75
p90u_75 and p10u_75
p00u_75 and p10u_75
In [27]:
df
Out[27]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
0 1.352765 0.251194 -0.464402 -0.508016 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 -0.282518 ... -1.172443 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829 -0.636833 0.605681
1 -0.126329 -0.064588 -0.774040 -0.731805 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 -0.404765 ... -0.586885 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095 -0.156917 0.514631
2 -4.916056 -0.579314 -1.002907 -0.916580 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 -0.576372 ... 0.414370 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894 -0.068467 -1.670572
3 -0.469286 -1.226995 -0.889115 -0.813688 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 -0.655143 ... -1.145335 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664 -0.593155 1.060932
4 -0.020197 0.018589 0.051419 0.029799 0.051246 0.176309 0.148003 0.179467 -0.085040 -0.085873 ... -0.741186 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095 -0.473250 0.514631
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 -1.506439 1.501828 1.792834 1.899641 1.913771 0.839405 1.051898 1.110167 2.454943 2.384831 ... 1.727114 2.251499 2.276462 1.196198 1.746700 1.828051 2.090747 2.335818 2.210780 -1.397422
272 0.096301 -0.957393 -0.854920 -0.820254 -0.771264 -0.789761 -0.786267 -0.743154 -0.772925 -0.718363 ... 0.245594 0.188036 0.253678 0.620326 0.569365 0.671899 -0.509843 -0.477951 -0.437206 1.060932
273 -0.461599 1.689799 2.357024 2.340021 2.270520 2.538004 2.634711 2.644594 1.762702 1.682191 ... 0.566912 0.570582 0.541617 0.867854 0.941671 0.931923 -0.165667 -0.201114 -0.199622 -0.395870
274 0.461080 0.470339 0.061055 -0.037393 -0.131383 -0.141744 -0.234249 -0.340844 0.257537 0.153308 ... -0.455104 -0.551699 -0.640980 -0.537094 -0.643647 -0.744355 -0.153133 -0.229645 -0.287221 0.878832
275 0.074844 -1.154910 -0.616189 -0.577300 -0.524737 -0.479518 -0.452401 -0.391292 -0.648693 -0.600271 ... -0.497452 -0.464913 -0.420298 -0.360175 -0.330085 -0.282576 -0.574150 -0.530945 -0.488370 1.060932

276 rows × 57 columns

In [28]:
from sklearn.decomposition import PCA
# Separate features and target variables
features = df.drop([  "p00_1200",  "p10_1200",  "p00u_1200",  "p10u_1200",  "p00r_1200",  "p10r_1200",  "p00_30",  "p10_30",  "p00u_30",  "p10u_30",  "p00r_30",  "p10r_30",  "p00_75",  "p10_75",  "p00u_75",  "p10u_75",  "p00r_75",  "p10r_75",  "p00_150",  "p10_150",  "p00u_150",  "p10u_150",  "p00r_150",  "p10r_150",  "p00_600",  "p10_600",  "p00u_600",  "p10u_600",  "p00r_600",  "p10r_600",  "p00_300",  "p10_300",  "p00u_300",  "p10u_300",  "p00r_300",  "p10r_300"], axis=1)
target_variables = df[[  "p00_1200",  "p10_1200",  "p00u_1200",  "p10u_1200",  "p00r_1200",  "p10r_1200",  "p00_30",  "p10_30",  "p00u_30",  "p10u_30",  "p00r_30",  "p10r_30",  "p00_75",  "p10_75",  "p00u_75",  "p10u_75",  "p00r_75",  "p10r_75",  "p00_150",  "p10_150",  "p00u_150",  "p10u_150",  "p00r_150",  "p10r_150",  "p00_600",  "p10_600",  "p00u_600",  "p10u_600",  "p00r_600",  "p10r_600",  "p00_300",  "p10_300",  "p00u_300",  "p10u_300",  "p00r_300",  "p10r_300"]]

# Apply dimensionality reduction using PCA
pca = PCA(n_components=2)  # Set the desired number of components
features_reduced = pca.fit_transform(features)

# Create a new DataFrame for the reduced features
df_reduced = pd.DataFrame(features_reduced, columns=['PC1', 'PC2'])

# Concatenate the reduced features with the target variables
df_combined = pd.concat([df_reduced, target_variables], axis=1)
In [29]:
df_reduced
Out[29]:
PC1 PC2
0 -2.329699 1.201499
1 -2.724107 -0.899067
2 -0.970523 -0.068136
3 -3.801980 -0.339716
4 -1.927433 -0.712879
... ... ...
271 3.797920 -2.980111
272 -1.871871 0.237314
273 2.285090 -1.486060
274 -1.308275 -0.415028
275 -1.430205 2.411526

276 rows × 2 columns

In [30]:
target_variables
Out[30]:
p00_1200 p10_1200 p00u_1200 p10u_1200 p00r_1200 p10r_1200 p00_30 p10_30 p00u_30 p10u_30 ... p00u_600 p10u_600 p00r_600 p10r_600 p00_300 p10_300 p00u_300 p10u_300 p00r_300 p10r_300
0 -0.508016 -0.554848 -0.660152 -0.734917 -0.282518 -0.308726 1.307545 1.234529 1.464840 1.379198 ... -1.198856 -1.233763 -0.622001 -0.599107 -1.136296 -1.125151 -1.198046 -1.215751 -0.675829 -0.636833
1 -0.731805 -0.679515 -0.953322 -0.919337 -0.404765 -0.361318 -0.607961 -0.582347 -0.534148 -0.512303 ... -0.931959 -0.907064 -0.360560 -0.317110 -0.600261 -0.556108 -0.736024 -0.708998 -0.193095 -0.156917
2 -0.916580 -0.857975 -1.120005 -1.079853 -0.576372 -0.526636 -0.483534 -0.456906 -0.410897 -0.388234 ... -0.326982 -0.245381 -0.386586 -0.339928 0.496365 0.605076 0.779507 0.935261 -0.111894 -0.068467
3 -0.813688 -0.764634 -0.841270 -0.801030 -0.655143 -0.609732 -0.558129 -0.534108 -0.468648 -0.447666 ... -1.038341 -1.026704 -0.628250 -0.584963 -1.094991 -1.071174 -1.162423 -1.166418 -0.638664 -0.593155
4 0.029799 0.051246 0.148003 0.179467 -0.085873 -0.068557 -0.600080 -0.574276 -0.543570 -0.521872 ... -0.713870 -0.672779 -0.408781 -0.367127 -0.728155 -0.685837 -0.714859 -0.682794 -0.517095 -0.473250
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 1.899641 1.913771 1.051898 1.110167 2.384831 2.304732 -0.392303 -0.377247 -0.430769 -0.413749 ... 0.717522 0.768739 1.553292 1.483419 2.251499 2.276462 1.746700 1.828051 2.335818 2.210780
272 -0.820254 -0.771264 -0.786267 -0.743154 -0.718363 -0.671430 -0.549863 -0.526056 -0.473721 -0.452860 ... -0.051293 0.024048 -0.615747 -0.572764 0.188036 0.253678 0.569365 0.671899 -0.477951 -0.437206
273 2.340021 2.270520 2.634711 2.644594 1.682191 1.579212 -0.446240 -0.434000 -0.480364 -0.462969 ... 0.077725 0.059601 0.144024 0.120417 0.570582 0.541617 0.941671 0.931923 -0.201114 -0.199622
274 -0.037393 -0.131383 -0.234249 -0.340844 0.153308 0.072083 -0.195625 -0.247802 -0.062048 -0.120870 ... -0.755498 -0.870089 -0.144182 -0.223553 -0.551699 -0.640980 -0.643647 -0.744355 -0.229645 -0.287221
275 -0.577300 -0.524737 -0.452401 -0.391292 -0.600271 -0.556288 0.277647 0.303837 0.480277 0.499443 ... -0.488171 -0.440802 -0.578217 -0.535978 -0.464913 -0.420298 -0.330085 -0.282576 -0.530945 -0.488370

276 rows × 36 columns

In [31]:
df_combined
Out[31]:
PC1 PC2 p00_1200 p10_1200 p00u_1200 p10u_1200 p00r_1200 p10r_1200 p00_30 p10_30 ... p00u_600 p10u_600 p00r_600 p10r_600 p00_300 p10_300 p00u_300 p10u_300 p00r_300 p10r_300
0 -2.329699 1.201499 -0.508016 -0.554848 -0.660152 -0.734917 -0.282518 -0.308726 1.307545 1.234529 ... -1.198856 -1.233763 -0.622001 -0.599107 -1.136296 -1.125151 -1.198046 -1.215751 -0.675829 -0.636833
1 -2.724107 -0.899067 -0.731805 -0.679515 -0.953322 -0.919337 -0.404765 -0.361318 -0.607961 -0.582347 ... -0.931959 -0.907064 -0.360560 -0.317110 -0.600261 -0.556108 -0.736024 -0.708998 -0.193095 -0.156917
2 -0.970523 -0.068136 -0.916580 -0.857975 -1.120005 -1.079853 -0.576372 -0.526636 -0.483534 -0.456906 ... -0.326982 -0.245381 -0.386586 -0.339928 0.496365 0.605076 0.779507 0.935261 -0.111894 -0.068467
3 -3.801980 -0.339716 -0.813688 -0.764634 -0.841270 -0.801030 -0.655143 -0.609732 -0.558129 -0.534108 ... -1.038341 -1.026704 -0.628250 -0.584963 -1.094991 -1.071174 -1.162423 -1.166418 -0.638664 -0.593155
4 -1.927433 -0.712879 0.029799 0.051246 0.148003 0.179467 -0.085873 -0.068557 -0.600080 -0.574276 ... -0.713870 -0.672779 -0.408781 -0.367127 -0.728155 -0.685837 -0.714859 -0.682794 -0.517095 -0.473250
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 3.797920 -2.980111 1.899641 1.913771 1.051898 1.110167 2.384831 2.304732 -0.392303 -0.377247 ... 0.717522 0.768739 1.553292 1.483419 2.251499 2.276462 1.746700 1.828051 2.335818 2.210780
272 -1.871871 0.237314 -0.820254 -0.771264 -0.786267 -0.743154 -0.718363 -0.671430 -0.549863 -0.526056 ... -0.051293 0.024048 -0.615747 -0.572764 0.188036 0.253678 0.569365 0.671899 -0.477951 -0.437206
273 2.285090 -1.486060 2.340021 2.270520 2.634711 2.644594 1.682191 1.579212 -0.446240 -0.434000 ... 0.077725 0.059601 0.144024 0.120417 0.570582 0.541617 0.941671 0.931923 -0.201114 -0.199622
274 -1.308275 -0.415028 -0.037393 -0.131383 -0.234249 -0.340844 0.153308 0.072083 -0.195625 -0.247802 ... -0.755498 -0.870089 -0.144182 -0.223553 -0.551699 -0.640980 -0.643647 -0.744355 -0.229645 -0.287221
275 -1.430205 2.411526 -0.577300 -0.524737 -0.452401 -0.391292 -0.600271 -0.556288 0.277647 0.303837 ... -0.488171 -0.440802 -0.578217 -0.535978 -0.464913 -0.420298 -0.330085 -0.282576 -0.530945 -0.488370

276 rows × 38 columns

In [32]:
# Define the number of folds for cross-validation
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)

# Assuming you have multiple target variables named 'target_variable1', 'target_variable2', etc.
target_variables = [
    "p00_1200", "p10_1200", "p00u_1200", "p10u_1200", "p00r_1200", "p10r_1200",
    "p00_30", "p10_30", "p00u_30", "p10u_30", "p00r_30", "p10r_30",
    "p00_75", "p10_75", "p00u_75", "p10u_75", "p00r_75", "p10r_75",
    "p00_150", "p10_150", "p00u_150", "p10u_150", "p00r_150", "p10r_150",
    "p00_600", "p10_600", "p00u_600", "p10u_600", "p00r_600", "p10r_600",
    "p00_300", "p10_300", "p00u_300", "p10u_300", "p00r_300", "p10r_300"
]
In [33]:
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np

# Define the number of folds for cross-validation
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)

# Define lists to store the errors for each target variable
rmse_list = []
mae_list = []

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(df_combined.drop(target_variables, axis=1),
                                                    df_combined[target_variables],
                                                    test_size=0.2,
                                                    random_state=42)

# Iterate over the folds
for train_index, val_index in kf.split(X_train):
    # Get the training and validation sets
    X_train_fold, X_val = X_train.iloc[train_index], X_train.iloc[val_index]
    y_train_fold, y_val = y_train.iloc[train_index], y_train.iloc[val_index]

    
In [35]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error
import numpy as np

# Initialize the Random Forest regressor
model1 = RandomForestRegressor(n_estimators=100, random_state=42)

# Fit the model on the training fold
model1.fit(X_train_fold, y_train_fold)

# Predict on the validation set
y_pred_val1 = model1.predict(X_val)

# Define lists to store the errors for each target variable
rmse_list2 = []
mae_list2 = []

# Calculate the evaluation metrics (RMSE and MAE) for each target variable
for i, target_variable in enumerate(target_variables):
    rmse_val1 = np.sqrt(mean_squared_error(y_val[target_variable], y_pred_val1[:, i]))
    mae_val1 = mean_absolute_error(y_val[target_variable], y_pred_val1[:, i])

    # Append the errors to the respective lists
    rmse_list2.append(rmse_val1)
    mae_list2.append(mae_val1)

    print("RMSE for", target_variable, ":", rmse_val1)
    print("MAE for", target_variable, ":", mae_val1)
RMSE for p00_1200 : 0.28053487099249796
MAE for p00_1200 : 0.1948436912548931
RMSE for p10_1200 : 0.3206282093604051
MAE for p10_1200 : 0.20642821311331963
RMSE for p00u_1200 : 0.3565035541850463
MAE for p00u_1200 : 0.26555113821735593
RMSE for p10u_1200 : 0.3622224277524904
MAE for p10u_1200 : 0.27207519064585567
RMSE for p00r_1200 : 0.47642499041156905
MAE for p00r_1200 : 0.18388689357518662
RMSE for p10r_1200 : 0.5218983496828521
MAE for p10r_1200 : 0.18426611788619127
RMSE for p00_30 : 0.33799139722089244
MAE for p00_30 : 0.200106464483822
RMSE for p10_30 : 0.3393117880326379
MAE for p10_30 : 0.19360788731632186
RMSE for p00u_30 : 0.37403049986873416
MAE for p00u_30 : 0.22111349088298954
RMSE for p10u_30 : 0.36165187894661205
MAE for p10u_30 : 0.20986730541809792
RMSE for p00r_30 : 1.089285288788809
MAE for p00r_30 : 0.3349170967042949
RMSE for p10r_30 : 1.155367559660732
MAE for p10r_30 : 0.33726318152906315
RMSE for p00_75 : 0.4116671794902733
MAE for p00_75 : 0.22409868930526253
RMSE for p10_75 : 0.4346887488196657
MAE for p10_75 : 0.22667021890827496
RMSE for p00u_75 : 0.39569698453578894
MAE for p00u_75 : 0.21799179733602395
RMSE for p10u_75 : 0.3942956845865981
MAE for p10u_75 : 0.21414695485501037
RMSE for p00r_75 : 1.2143681756879294
MAE for p00r_75 : 0.33294696940790414
RMSE for p10r_75 : 1.28368633089651
MAE for p10r_75 : 0.33357030579982994
RMSE for p00_150 : 0.7169390747541178
MAE for p00_150 : 0.3459846141737994
RMSE for p10_150 : 0.7855824402944899
MAE for p10_150 : 0.35603813052017
RMSE for p00u_150 : 0.5129264113113432
MAE for p00u_150 : 0.35741779300718685
RMSE for p10u_150 : 0.5422900688976562
MAE for p10u_150 : 0.36746998695269467
RMSE for p00r_150 : 1.205820247489527
MAE for p00r_150 : 0.29594483970705054
RMSE for p10r_150 : 1.2662874090582625
MAE for p10r_150 : 0.29457922878080894
RMSE for p00_600 : 0.24538479594815193
MAE for p00_600 : 0.14214559061796664
RMSE for p10_600 : 0.29085383984553653
MAE for p10_600 : 0.1494993988723669
RMSE for p00u_600 : 0.32346407368869823
MAE for p00u_600 : 0.23903389700483865
RMSE for p10u_600 : 0.324437060087517
MAE for p10u_600 : 0.2424339395463642
RMSE for p00r_600 : 0.5837132406335707
MAE for p00r_600 : 0.19380854468077482
RMSE for p10r_600 : 0.6311034000067061
MAE for p10r_600 : 0.1911446771486567
RMSE for p00_300 : 0.4718167875575159
MAE for p00_300 : 0.2674840344721814
RMSE for p10_300 : 0.5207533074305426
MAE for p10_300 : 0.2815552539663015
RMSE for p00u_300 : 0.4506709386820878
MAE for p00u_300 : 0.30844367851577875
RMSE for p10u_300 : 0.4730017497075956
MAE for p10u_300 : 0.3238295224995055
RMSE for p00r_300 : 0.818520411878747
MAE for p00r_300 : 0.22149873802055808
RMSE for p10r_300 : 0.8568042640036099
MAE for p10r_300 : 0.21955155259077025
In [37]:
# Initialize the Random Forest regressor
model1 = RandomForestRegressor(n_estimators=100, random_state=42)

# Fit the model on the entire training set
model1.fit(X_train, y_train)

# Predict on the test set
y_pred_test1 = model1.predict(X_test)

# Define lists to store the errors for each target variable
rmse_list3 = []
mae_list3 = []

# Calculate the evaluation metrics (RMSE and MAE) for each target variable on the test set
for i, target_variable in enumerate(target_variables):
    rmse_test1 = np.sqrt(mean_squared_error(y_test[target_variable], y_pred_test1[:, i]))
    mae_test1 = mean_absolute_error(y_test[target_variable], y_pred_test1[:, i])

    # Append the errors to the respective lists
    rmse_list3.append(rmse_test1)
    mae_list3.append(mae_test1)

    print("Test Set - RMSE for", target_variable, ":", rmse_test1)
    print("Test Set - MAE for", target_variable, ":", mae_test1)
Test Set - RMSE for p00_1200 : 0.45280389201585375
Test Set - MAE for p00_1200 : 0.2923768780241364
Test Set - RMSE for p10_1200 : 0.4656432253501561
Test Set - MAE for p10_1200 : 0.2922645250991608
Test Set - RMSE for p00u_1200 : 0.508848962579067
Test Set - MAE for p00u_1200 : 0.33957099187181466
Test Set - RMSE for p10u_1200 : 0.5284038193915708
Test Set - MAE for p10u_1200 : 0.355275704426346
Test Set - RMSE for p00r_1200 : 0.46037116621014496
Test Set - MAE for p00r_1200 : 0.2543251069726749
Test Set - RMSE for p10r_1200 : 0.4704835818949202
Test Set - MAE for p10r_1200 : 0.23690445526606377
Test Set - RMSE for p00_30 : 0.5773095102798528
Test Set - MAE for p00_30 : 0.2900682542197274
Test Set - RMSE for p10_30 : 0.5005143704087691
Test Set - MAE for p10_30 : 0.26423216443018277
Test Set - RMSE for p00u_30 : 0.6358139995390857
Test Set - MAE for p00u_30 : 0.2999998645748043
Test Set - RMSE for p10u_30 : 0.5459368296141377
Test Set - MAE for p10u_30 : 0.2705874461158171
Test Set - RMSE for p00r_30 : 0.5442335652480189
Test Set - MAE for p00r_30 : 0.3090926860882162
Test Set - RMSE for p10r_30 : 0.5474480246604531
Test Set - MAE for p10r_30 : 0.3066609688663523
Test Set - RMSE for p00_75 : 0.3533560511009237
Test Set - MAE for p00_75 : 0.2251331898052143
Test Set - RMSE for p10_75 : 0.3674915025837975
Test Set - MAE for p10_75 : 0.23006098314645068
Test Set - RMSE for p00u_75 : 0.374789062484832
Test Set - MAE for p00u_75 : 0.237680399324769
Test Set - RMSE for p10u_75 : 0.3794550173699248
Test Set - MAE for p10u_75 : 0.24138659244056507
Test Set - RMSE for p00r_75 : 0.5171583569175715
Test Set - MAE for p00r_75 : 0.2777236306994814
Test Set - RMSE for p10r_75 : 0.5276236667763128
Test Set - MAE for p10r_75 : 0.2753680055067304
Test Set - RMSE for p00_150 : 0.5634481767377898
Test Set - MAE for p00_150 : 0.3253371966932
Test Set - RMSE for p10_150 : 0.5877941042173158
Test Set - MAE for p10_150 : 0.33601448650851473
Test Set - RMSE for p00u_150 : 0.6317719209108424
Test Set - MAE for p00u_150 : 0.36724731023095875
Test Set - RMSE for p10u_150 : 0.6644140016736902
Test Set - MAE for p10u_150 : 0.3800719390982543
Test Set - RMSE for p00r_150 : 0.4197891778446871
Test Set - MAE for p00r_150 : 0.21572103927614003
Test Set - RMSE for p10r_150 : 0.4345369266202263
Test Set - MAE for p10r_150 : 0.21015104496946038
Test Set - RMSE for p00_600 : 0.2811236668624168
Test Set - MAE for p00_600 : 0.19529984481674828
Test Set - RMSE for p10_600 : 0.3087316531943777
Test Set - MAE for p10_600 : 0.21323143424064714
Test Set - RMSE for p00u_600 : 0.3563992151905941
Test Set - MAE for p00u_600 : 0.24838230840520037
Test Set - RMSE for p10u_600 : 0.38356833868655626
Test Set - MAE for p10u_600 : 0.26537127392980053
Test Set - RMSE for p00r_600 : 0.3379746211463177
Test Set - MAE for p00r_600 : 0.18612656159199456
Test Set - RMSE for p10r_600 : 0.3558154743548304
Test Set - MAE for p10r_600 : 0.18769353723603938
Test Set - RMSE for p00_300 : 0.42559849194244254
Test Set - MAE for p00_300 : 0.3107006820754439
Test Set - RMSE for p10_300 : 0.4461664951580049
Test Set - MAE for p10_300 : 0.32539219101383404
Test Set - RMSE for p00u_300 : 0.5513268289705853
Test Set - MAE for p00u_300 : 0.4119960761572056
Test Set - RMSE for p10u_300 : 0.5813459405496905
Test Set - MAE for p10u_300 : 0.4340357334863906
Test Set - RMSE for p00r_300 : 0.2763603526889699
Test Set - MAE for p00r_300 : 0.17326067914672577
Test Set - RMSE for p10r_300 : 0.282141141087941
Test Set - MAE for p10r_300 : 0.17144293171428357
In [39]:
# Calculate the mean error for each metric
mean_rmse2 = np.mean(rmse_list2)
mean_mae2 = np.mean(mae_list2)

print("Mean RMSE TRAIN:", mean_rmse2)
print("Mean MAE TRAIN:", mean_mae2)

mean_rmse3 = np.mean(rmse_list3)
mean_mae3 = np.mean(mae_list3)

print("Mean RMSE TEST:", mean_rmse3)
print("Mean MAE TEST:", mean_mae3)
Mean RMSE TRAIN: 0.5869617622276588
Mean MAE TRAIN: 0.25420041743659644
Mean RMSE TEST: 0.4623886425628519
Mean MAE TEST: 0.2765607810408153
In [42]:
from sklearn.metrics import r2_score

# Calculate the R-squared for the test set
r2_test = r2_score(y_test, y_pred_test1)

# Calculate the adjusted R-squared for the test set
n_test = X_test.shape[0]  # Number of samples in the test set
p_test = X_test.shape[1]  # Number of features in the test set
adj_r2_test = 1 - (1 - r2_test) * ((n_test - 1) / (n_test - p_test - 1))

# Print the adjusted R-squared for each target variable in the test set
print("Adjusted R^2 (Test):")
for i, target_variable in enumerate(target_variables):
    print(target_variable, ":", adj_r2_test)

# Calculate the mean of the training set
mean_train = np.mean(y_train, axis=0)

# Calculate the mean of the test set
mean_test = np.mean(y_test, axis=0)

# Print the mean of the training set for each target variable
print("Mean (Train):")
for i, target_variable in enumerate(target_variables):
    print(target_variable, ":", mean_train[i])

# Print the mean of the test set for each target variable
print("Mean (Test):")
for i, target_variable in enumerate(target_variables):
    print(target_variable, ":", mean_test[i])
Adjusted R^2 (Test):
p00_1200 : 0.7480867771763906
p10_1200 : 0.7480867771763906
p00u_1200 : 0.7480867771763906
p10u_1200 : 0.7480867771763906
p00r_1200 : 0.7480867771763906
p10r_1200 : 0.7480867771763906
p00_30 : 0.7480867771763906
p10_30 : 0.7480867771763906
p00u_30 : 0.7480867771763906
p10u_30 : 0.7480867771763906
p00r_30 : 0.7480867771763906
p10r_30 : 0.7480867771763906
p00_75 : 0.7480867771763906
p10_75 : 0.7480867771763906
p00u_75 : 0.7480867771763906
p10u_75 : 0.7480867771763906
p00r_75 : 0.7480867771763906
p10r_75 : 0.7480867771763906
p00_150 : 0.7480867771763906
p10_150 : 0.7480867771763906
p00u_150 : 0.7480867771763906
p10u_150 : 0.7480867771763906
p00r_150 : 0.7480867771763906
p10r_150 : 0.7480867771763906
p00_600 : 0.7480867771763906
p10_600 : 0.7480867771763906
p00u_600 : 0.7480867771763906
p10u_600 : 0.7480867771763906
p00r_600 : 0.7480867771763906
p10r_600 : 0.7480867771763906
p00_300 : 0.7480867771763906
p10_300 : 0.7480867771763906
p00u_300 : 0.7480867771763906
p10u_300 : 0.7480867771763906
p00r_300 : 0.7480867771763906
p10r_300 : 0.7480867771763906
Mean (Train):
p00_1200 : -0.04516698280082065
p10_1200 : -0.04530860553563185
p00u_1200 : -0.03606130414862029
p10u_1200 : -0.03657654740196988
p00r_1200 : -0.04634645070212615
p10r_1200 : -0.04561417782626307
p00_30 : -0.019903332213403867
p10_30 : -0.015143898945274558
p00u_30 : -0.022858175861743327
p10u_30 : -0.01796218216905223
p00r_30 : 0.004775071550874152
p10r_30 : 0.00622943354298093
p00_75 : -0.0038051759838948446
p10_75 : -0.0015021363601061411
p00u_75 : -0.002774865462601772
p10u_75 : -0.0004571324316705533
p00r_75 : -0.0050741720841499345
p10r_75 : -0.0038687765180764927
p00_150 : 0.008511861411043408
p10_150 : 0.009158548709518315
p00u_150 : 0.013756918171685933
p10u_150 : 0.014348939766580703
p00r_150 : -0.006737598983122436
p10r_150 : -0.005438501559455057
p00_600 : -0.017186524991065294
p10_600 : -0.01761569853034427
p00u_600 : -0.010346543012594326
p10u_600 : -0.010567661218205467
p00r_600 : -0.02147201395543675
p10r_600 : -0.021356990478684168
p00_300 : -0.011591097601375834
p10_300 : -0.010689836397070525
p00u_300 : -0.005706823105841629
p10u_300 : -0.004751638462402846
p00r_300 : -0.01724637497710254
p10r_300 : -0.015976742617020593
Mean (Test):
p00_1200 : 0.17744171814608106
p10_1200 : 0.17799809317569668
p00u_1200 : 0.14166940915529447
p10u_1200 : 0.14369357907916783
p00r_1200 : 0.182075342044067
p10r_1200 : 0.17919855574603374
p00_30 : 0.07819166226694443
p10_30 : 0.05949388871357815
p00u_30 : 0.08979997659970547
p10u_30 : 0.07056571566413351
p00r_30 : -0.018759209664148912
p10r_30 : -0.02447277463313952
p00_75 : 0.014948905651015698
p10_75 : 0.005901249986131444
p00u_75 : 0.010901257174506724
p10u_75 : 0.0017958774101349353
p00r_75 : 0.019934247473446094
p10r_75 : 0.01519876489244319
p00_150 : -0.03343945554338528
p10_150 : -0.03598001278739349
p00u_150 : -0.054045035674480255
p10u_150 : -0.056370834797282096
p00r_150 : 0.026469138862266716
p10r_150 : 0.021365541840715707
p00_600 : 0.06751849103632857
p10_600 : 0.06920452994063839
p00u_600 : 0.04064713326376405
p10u_600 : 0.041515811928664985
p00r_600 : 0.08435434053921606
p10r_600 : 0.08390246259483028
p00_300 : 0.04553645486254781
p10_300 : 0.04199578584563344
p00u_300 : 0.022419662201520713
p10u_300 : 0.01866715110229638
p00r_300 : 0.06775361598147389
p10r_300 : 0.06276577456686654
In [ ]: